COP-and-Debiasing.ipynb

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     "end_time": "2023-07-13T18:15:07.589527Z",
     "start_time": "2023-07-13T18:15:05.671458Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import graphviz as gr\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0f02c93",
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    "ExecuteTime": {
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     "start_time": "2023-07-13T18:15:07.591117Z"
    }
   },
   "outputs": [
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   "source": [
    "cop_dag = gr.Digraph()\n",
    "\n",
    "cop_dag.edge(\"Health\", \"Model\", color=\"red\")\n",
    "cop_dag.edge(\"Model\", \"Price\", color=\"blue\")\n",
    "cop_dag.edge(\"Price\", \"Conv\", color=\"blue\")\n",
    "cop_dag.edge(\"Health\", \"Conv\", color=\"red\")\n",
    "\n",
    "cop_dag.edge(\"Time\", \"Conv\", color=\"red\")\n",
    "cop_dag.edge(\"Time\", \"Claim\", color=\"red\")\n",
    "\n",
    "cop_dag.edge(\"Health\", \"Claim\", color=\"red\")\n",
    "cop_dag.edge(\"Conv\", \"Claim\", color=\"red\")\n",
    "cop_dag.edge(\"Price\", \"Claim\", color=\"red\")\n",
    "\n",
    "cop_dag.node(\"Conv\", color=\"lightgrey\", style=\"filled\")\n",
    "cop_dag.node(\"Health\", style=\"dashed\")\n",
    "\n",
    "cop_dag"
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   "cell_type": "code",
   "execution_count": 3,
   "id": "5082119d",
   "metadata": {
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     "end_time": "2023-07-13T18:15:07.812303Z",
     "start_time": "2023-07-13T18:15:07.806355Z"
    }
   },
   "outputs": [],
   "source": [
    "price_assignment = pd.DataFrame(dict(\n",
    "    price = [20,40,60,80,   20,40,60,80, 20,40,60,80, 20,40,60,80],\n",
    "    ps =    [.4,.3,.2,.1,  .3,.3,.2,.2, .2,.2,.3,.3, .1,.2,.3,.4,],\n",
    "    model = [1,1,1,1,       2,2,2,2,     3,3,3,3,     4,4,4,4,]\n",
    ")).set_index(\"model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48092e64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:20.883884Z",
     "start_time": "2023-07-13T18:15:07.814533Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "health       5.503251\n",
      "time         6.503860\n",
      "price       49.468200\n",
      "conv         0.838120\n",
      "model        2.414050\n",
      "claim        0.605680\n",
      "conv_20      0.915040\n",
      "conv_40      0.867930\n",
      "conv_60      0.809580\n",
      "conv_80      0.741680\n",
      "claim_20     0.350760\n",
      "claim_40     0.592320\n",
      "claim_60     0.774160\n",
      "claim_80     0.741680\n",
      "ps           0.275542\n",
      "dtype: float64\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>health</th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "      <th>conv</th>\n",
       "      <th>model</th>\n",
       "      <th>claim</th>\n",
       "      <th>conv_20</th>\n",
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       "      <td>1.736504</td>\n",
       "      <td>3</td>\n",
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       "      <td>2.991294</td>\n",
       "      <td>12</td>\n",
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      ],
      "text/plain": [
       "     health  time  price  conv  model  claim  conv_20  conv_40  conv_60  \\\n",
       "0  1.736504     3     60     0      1      0        0        0        0   \n",
       "1  2.991294    12     60     1      1      1        1        1        1   \n",
       "2  2.492579     9     60     1      1      1        1        1        1   \n",
       "3  2.000869    10     60     1      1      1        1        1        1   \n",
       "4  1.968288     2     60     0      1      0        0        0        0   \n",
       "\n",
       "   conv_80  claim_20  claim_40  claim_60  claim_80   ps  \n",
       "0        0         0         0         0         0  0.2  \n",
       "1        1         0         1         1         1  0.2  \n",
       "2        1         0         0         1         1  0.2  \n",
       "3        1         0         0         1         1  0.2  \n",
       "4        0         0         0         0         0  0.2  "
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "n = 100000\n",
    "\n",
    "np.random.seed(123)\n",
    "\n",
    "health = np.random.beta(2,2, n)*11\n",
    "model = (np.round(np.random.beta(health/health.mean(), 1)*3)+1).astype(int)\n",
    "time = np.random.randint(1, 13, n)\n",
    "\n",
    "price = np.array([np.random.choice(price_assignment.loc[m][\"price\"], p=price_assignment.loc[m][\"ps\"])\n",
    "                  for m in model])\n",
    "\n",
    "def conv_fn(p):\n",
    "    return ((-0.05*p + health + time) > 5)*1\n",
    "\n",
    "\n",
    "def claim_fn(p):\n",
    "    return ((health + 0.1*p + 0.5*time) > 12)*1\n",
    "\n",
    "\n",
    "conv = conv_fn(price)\n",
    "claim = claim_fn(price)\n",
    "\n",
    "df_cop = pd.DataFrame(dict(\n",
    "    health=health,\n",
    "    time=time,\n",
    "    price=price,\n",
    "    conv=conv,\n",
    "    model=model,\n",
    "    claim=claim*conv,\n",
    "    **{**{f\"conv_{p}\": conv_fn(p) for p in np.unique(price)}}\n",
    "))\n",
    "\n",
    "\n",
    "df_cop = (df_cop\n",
    "          .assign(**{f\"claim_{p}\": df_cop[f\"conv_{p}\"]*claim_fn(p) for p in np.unique(price)})\n",
    "          .merge(price_assignment.reset_index(), on=[\"price\", \"model\"]))\n",
    "\n",
    "print(df_cop.mean())\n",
    "df_cop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89b9ebae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:20.903794Z",
     "start_time": "2023-07-13T18:15:20.885139Z"
    }
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52916</th>\n",
       "      <td>7.134701</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52917</th>\n",
       "      <td>6.146034</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>1.934520</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>7.061395</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>4.334269</td>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>5.042518</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>3.462463</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25139 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          health  time  price  conv  model  claim  conv_20  conv_40  conv_60  \\\n",
       "52913  10.719150    10     40     1      4      1        1        1        1   \n",
       "52914   2.699107     2     40     0      4      0        0        0        0   \n",
       "52915   3.872851     9     40     1      4      1        1        1        1   \n",
       "52916   7.134701     3     40     1      4      1        1        1        1   \n",
       "52917   6.146034     2     40     1      4      0        1        1        1   \n",
       "...          ...   ...    ...   ...    ...    ...      ...      ...      ...   \n",
       "99995   1.934520     1     40     0      1      0        0        0        0   \n",
       "99996   7.061395     2     40     1      1      1        1        1        1   \n",
       "99997   4.334269     8     40     1      1      1        1        1        1   \n",
       "99998   5.042518     2     40     1      1      0        1        1        0   \n",
       "99999   3.462463     6     40     1      1      0        1        1        1   \n",
       "\n",
       "       conv_80  claim_20  claim_40  claim_60  claim_80   ps  \n",
       "52913        1         1         1         1         1  0.2  \n",
       "52914        0         0         0         0         0  0.2  \n",
       "52915        1         0         1         1         1  0.2  \n",
       "52916        1         0         1         1         1  0.2  \n",
       "52917        0         0         0         1         0  0.2  \n",
       "...        ...       ...       ...       ...       ...  ...  \n",
       "99995        0         0         0         0         0  0.3  \n",
       "99996        1         0         1         1         1  0.3  \n",
       "99997        1         0         1         1         1  0.3  \n",
       "99998        0         0         0         0         0  0.3  \n",
       "99999        1         0         0         1         1  0.3  \n",
       "\n",
       "[25139 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cop.query(\"price==40\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e2115d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2c67e0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:21.210198Z",
     "start_time": "2023-07-13T18:15:20.905028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "[plt.hist(df_cop.query(f\"model==1\").query(f\"time=={t}\")[\"price\"], alpha=0.1, label=f\"model=1 time {t}\") for t in range(1,13)]\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac63118b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:21.331311Z",
     "start_time": "2023-07-13T18:15:21.211402Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "[plt.hist(df_cop.query(f\"model=={m}\")[\"price\"], alpha=0.2, label=f\"model {m}\") for m in df_cop[\"model\"].unique()]\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5858ad",
   "metadata": {},
   "source": [
    "## Adjusting for Confounders\n",
    "\n",
    "### IPW\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tau &= E\\bigg[\\dfrac{1(T=1)Y}{e(x)} \\bigg] - E\\bigg[\\dfrac{1(T=0)Y}{1-e(x)} \\bigg]\\\\\n",
    "&=E\\bigg[Y\\dfrac{1(T=1)(1-e(x)) - 1(T=0)e(x)}{e(x)(1-e(x))} \\bigg] \\\\\n",
    "&=E\\bigg[Y\\dfrac{T(1-e(x)) - (1-T)e(x)}{e(x)(1-e(x))} \\bigg] \\\\\n",
    "&=E\\bigg[Y\\dfrac{T-Te(x) - e(x) + Te(x)}{e(x)(1-e(x))} \\bigg] \\\\\n",
    "&=E\\bigg[Y\\dfrac{T- e(x)}{e(x)(1-e(x))} \\bigg] \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "\n",
    "### OLS\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tau &= \\bigg[\\dfrac{Y(T - E[T|X])}{(T - E[T|X])^2}\\bigg] \\\\\n",
    "&= \\bigg[\\dfrac{Y(T - E[T|X])}{(T - E[T|X])^2}\\bigg] \\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "615f8191",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:21.337341Z",
     "start_time": "2023-07-13T18:15:21.332946Z"
    }
   },
   "outputs": [],
   "source": [
    "def estimate_ps(df, x=[\"model\"], t=\"price\"):\n",
    "     ps_dataset = (df\n",
    "                  .groupby(x+[t])\n",
    "                  .size()\n",
    "                  .reset_index()\n",
    "                  .assign(\n",
    "                      ps_est = lambda d: d[0]/d.groupby(x)[0].transform(\"sum\"),\n",
    "                  )\n",
    "                  .assign(w_est = lambda d: 1/d[\"ps_est\"])\n",
    "                  [x+[t, \"w_est\", \"ps_est\"]])\n",
    "    \n",
    "     return (df\n",
    "             .merge(ps_dataset, on=x+[t]))\n",
    "\n",
    "    \n",
    "def orthogonalize(df, x=[\"model\"], t=\"price\"):\n",
    "    mu = df[t].mean()\n",
    "    return (\n",
    "        df.assign(**{\n",
    "            t+\"_deb\":  mu + df[t] - df.groupby(x)[t].transform(\"mean\")\n",
    "        })\n",
    "    )\n",
    "\n",
    "    \n",
    "    \n",
    "def estimate_factual_curve(df, y, x=[\"model\", \"time\"], t=\"price\", weight=\"w\"):\n",
    "\n",
    "    return (df\n",
    "            .groupby(t)\n",
    "            .apply(lambda d: np.average(d[y], weights=d[weight])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5257c9",
   "metadata": {},
   "source": [
    "## Effect of Price on Conversion\n",
    "\n",
    "### Biased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c250f1ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:21.971906Z",
     "start_time": "2023-07-13T18:15:21.338678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_conv_curve = (df_cop[[f\"conv_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                 .melt()\n",
    "                 .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                 .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "factual_conv_curve = (df_cop\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"conv\"])))\n",
    "\n",
    "plt.plot(cf_conv_curve, label=\"P(conv(price))\")\n",
    "plt.plot(factual_conv_curve, label=\"P(conv|price)\")\n",
    "\n",
    "plt.ylabel(\"Conversion\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f6da975",
   "metadata": {},
   "source": [
    "### Using IPW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4dea23d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:22.582656Z",
     "start_time": "2023-07-13T18:15:21.975175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_conv_curve = (df_cop[[f\"conv_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                 .melt()\n",
    "                 .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                 .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "factual_conv_curve = (df_cop.assign(w=lambda d: 1/d[\"ps\"])\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"conv\"], weights=d[\"w\"])))\n",
    "\n",
    "plt.plot(cf_conv_curve, label=\"P(conv(price))\")\n",
    "plt.plot(factual_conv_curve, label=\"P(conv|price)\")\n",
    "\n",
    "plt.ylabel(\"Conversion\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5bb0bb4",
   "metadata": {},
   "source": [
    "### Using Orthogonalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c299368",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:23.247280Z",
     "start_time": "2023-07-13T18:15:22.584147Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_conv_curve = (df_cop[[f\"conv_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                 .melt()\n",
    "                 .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                 .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "ortho_df = orthogonalize(orthogonalize(df_cop, x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"conv\")\n",
    "\n",
    "factual_conv_curve = (ortho_df\n",
    "                      .groupby(\"price_deb\")\n",
    "                      .apply(lambda d: np.average(d[\"conv_deb\"])))\n",
    "\n",
    "plt.plot(cf_conv_curve, label=\"P(conv(price))\")\n",
    "plt.plot(factual_conv_curve, label=\"P(conv|price)\")\n",
    "\n",
    "sns.scatterplot(data=(ortho_df\n",
    "                      .groupby([\"price_deb\", \"model\"])[\"conv_deb\"]\n",
    "                      .mean().reset_index()),\n",
    "                x=\"price_deb\", y=\"conv_deb\", hue=\"model\")\n",
    "\n",
    "plt.ylabel(\"Conversion\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d247e82",
   "metadata": {},
   "source": [
    "## Effect of Price on Claims\n",
    "\n",
    "### Biased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87c8c7ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:23.789314Z",
     "start_time": "2023-07-13T18:15:23.248592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nbixheeDAAZYvX46vry/Tpk27sf3o0aNp06YNixYtIjk5mejoaPbu3cucOXNYu3Ytfn5+PPnkk8ycOZOBAwdStGhR4uPjOX/+/I3prpWFts+G756CkjW59thsXltxnkVbI2h5R3E+7d2Q0oVcPKW0wtdHKFckkHJFAmlxR/r/EzEJSTcuWt8YZHchhsPnrvH7gSjik27tEvvkvVV5sUPNXK/T84IgB3+556a8th5BRusN9+nT58bt2LFjbzz+2GOPZXjqauXKlXz77beA7RpI4cKFmT59Ops3b74xIV5sbCylSt38q/P6egQaBBYyBtZ+CsvfhJC72XvvBJ6a9hdHzl9jbLsajLq/msf1avFUQf75uLNMcIajuo0xRF2Nv3lt4nwsDSsVsaQOzwsCF8lr6xFkJPV7p/4+s/UIMmKMYdCgQbfMMJq2Dbr+gIVSkuGnl2Hjl5i63ZlZ9iXenryLokF+hA1vkeFfnco9iQilCgVQqlAAoSG3MbNpDugVJAulXo8AbFNST5kyhZiYGIB0p4YyWo8gN82ZM+fGbcuWLbPdvm3btkyYMAGA5ORkrly5Qtu2bZk/fz5nz54FbG04evQoYAuJ06dP3zgVpnJZYhzMHwIbvyS+6RM8GfMEr31/kFZVi/Pj6Ls1BNRt0yMCi7lqPYKMxMfH07x5c1JSUpg1a1a223/22WeMGDGCr7/+Gl9fXyZMmEDLli159913ad++PSkpKfj5+TF+/HgqV67M5s2badGiBfny6a9Vrou9ZFtM5ugaIpu9Rq8dTThzJYpXO9ViWOsqOl2C+lt09lGLuXI9gtTjCEJCQggPD7+x/rAVxowZQ+fOnWnbtq1D2+fln1uecvkEzOyBOXeQFTXfYuS2KpQpHMD/+jSiUSUds6Eco7OPupAr1yNwtrp16zocAspBZ/fCjO6kxF3mwxLvMmFLRTrWLc373eu75QhWlTdpEDjB0KFDXfK+zzzzzI3vczrC+HYMHz7c8vfwKkf/hFm9SRB/hqa8ycaTFXinSy36t6is8/CrXOUxQZBdjxxvlDoI8hp3OyXpdHuWYBY8zkX/MnS59Cx+xUNYNLRRhoOWlPq7PKLXUEBAAOfPn9cPFzdhjOH8+fMEBOiApwxt/AozdyAHfapw/8VXaNqwId8/3VpDQFnGI44IKlSoQGRkJFFRUa4uRTkoICCAChUquLqMvMUYWPE2rPmY3wllbNxoXnusCT2a6L+TspZHBIGfnx9VqlRxdRlK3b7kRFK+exqfHbMIS7qfGcVHM69fKNVKWb+OsFIeEQRKubX4aGLD+hN4dBUfJ/bgfJMxLHykjq65q5xGg0ApV4qO4vLXXSlwcQ9vmH/QrNdYHqpf1tVVKS+jQaCUi8SfOUj0110Iio/i34X/ybBBIzOd814pK1naa0hEOojIfhGJEJF0S16JyAsiss3+tUtEkkXE2tmVlMoDInevJXZiWyT+MrNrjeel0c9oCCiXsSwIRMQXGA90BGoDfUSkduptjDEfGmMaGmMaAi8Dvxtj0k/Sr5QH+fOnWRSb241rxp/9neYzpHdP/PN5RE9u5aas/O1rBkQYYw4ZYxKA2UCXLLbvA2Q/E5pSbiomIYk5k96n2bonOe1XgXwjVtCyefazwCplNSuvEZQHjqe6Hwk0z2hDEQkCOgCjMnl+BDACoFKlSrlbpVJOsP/UFdZMe5lh8TM4WqQZlf4xj3xBRVxdllKAtUGQ0XwPmQ39fQRYm9lpIWPMJGAS2GYfzZ3ylLKeMYY5G4+Q/MOLDPP5hbMhXajcfzLk83d1aUrdYOWpoUigYqr7FYCTmWzbGz0tpDzM1bhEngvbQOGlw+nn8wsxoU9RauA0DQGV51h5RLAJqC4iVYAT2D7s+6bdSEQKA22A/hbWopRT7Yy8zEthf/Bm9DuE+h4g5cH3CGr5pKvLUipDlgWBMSZJREYBPwO+wBRjzG4RGWl/fqJ9027AL8aYa1bVopSzGGOYuvYI05atZpr/B4TkO4M8OgWp+6irS1MqUx6xQplSecGlmASen7eD4/s2MSvoI4r6xiN9ZkGVu11dmlK6QplSVgs/coHRs7ZS5dpWlhT4BP/AYKTfd1CmrqtLUypbOopFqb8hJcUwflUEvSat5wHWMd3/P+QvUh4Z9quGgHIbekSg1G2KuhrPs3O3sfrgOT6qtI7uZ8chFZtDn1kQpDOlKPehQaDUbVhz8BzPzNlGdFw8y2r/Sq1DU6Hmw9B9MvgFuro8pXJEg0CpHEhKTuGzFQcZtyqCO0vkZ0WV+RQ+uBBCh0GnD8FH1xBQ7keDQCkHnbocy5hZ29h45AIDGhXjzbj/4HtwFdz/Gtz9PEhGg+mVyvs0CJRywIq9Z3h+3nYSklKY0KU8HXeMhtO7oMt4aKRjIZV70yBQKgsJSSl88NM+Jq85TO2yhZjYqTCVfugP16Kgz2yo0d7VJSr1t2kQKJWJY+djeHrWFrZHXmZQy8q80iCG/HO72p4ctBQqNHFpfUrlFg0CpTKwdMdJXl6wExGY2L8xHfx3wMzBULAU9F8Ixau6ukSlco0GgVKpxCUm8/bSPYRtOEajSkX4vHcjKh5ZAAvG2AaI9ZtvCwOlPIgGgVJ2EWejGRW2hX2nr/KPNnfw/AM18Fv7X1j1L6jaFnp+A/mDXV2mUrlOg0ApYP7mSP65eBdB/r5MG9KUe6sXhx+eg81ToUEf6Pw/8PVzdZlKWUKDQHm1a/FJ/HPxLhZuPUGLO4rxWe9GlA40MGcA7P8BWo+Ftm/oGAHl0TQIlNfac/IKo2Zt4ci5a4xtV4NR91fDN+4ifNsbjm+Ejh9C8xGuLlMpy2kQKK9jjGHGhmO8s3QPRQL9mPl4C1pWLQ6XjsGM7nDxKDw2Dep0dXWpSjmFBoHyKpdjE3lpwQ6W7TrNvXeW5L+PNaB4wfxweifM6AGJsTBgEYS0cnWpSjmNBoHyGtuOX2JU2BZOX47j5Y41GX73Hfj4CBz6Heb0t/UIGvoTlK7t6lKVcioNAuXxUlIMX685zH9+2kfpQgHMHdmSxpWK2p7cOR8WjYTi1aD/fChcwbXFKuUCGgTKo124lsBzc7exan8UHeqU4T/d61M4yN4NdN14+PkVqHQX9AmDwKKuLVYpF9EgUB5r/aHzjJm9lYvXEnmnSx36t6iMiEBKCvz6T1g3Dmp1hke/Ar8AV5erlMtoECiPk5xiGLcygs9WHCCkeAGmDG5KnXKFbU8mJcDiJ2DXfGg2Ajq8r4vJKK+nQaA8ytkrcYyZvY11h87TrVF53ulal4L57b/mcVdsF4UP/24bJNZ6rA4UUwoNAuVBfj8QxbNzthGTkMyHPerTo0kF26kggKunbd1Do/ZC14nQsI9ri1UqD9EgUG4vMTmFj389wITf/uLO0sGM79eIaqVSTQ537iBMfxRizkPfOVCtneuKVSoP0iBQbi3yYgyjZ21ly7FL9G1eidcfrk2AX6pz/sc3QlhP8MkHg5dC+cauK1apPEqDQLmtn3ef5oV520kx8L8+jXikQblbN9j3I8wfCoXKQv8FUOwO1xSqVB6nQaDcTnxSMu/9uI9pfx6hXvnCjOvbiMrFC9y60eZpsHQslG0IfedCwZKuKFUpt6BBoNzK4XPXeHrWFnaduMLQVlX4v453kj9fqlNBxsBv78Pv70O1B2yTx+Uv6LJ6lXIHGgTKbXy37QSvLNyJXz4fJg8MpV3t0rdukJwEPzwLW76Bhv3hkU91MRmlHKBBoPK82IRk3lyymznhxwmtXJTP+zSiXJHAWzdKiLFdDziwDO5+Hu5/TccIKOUgDQKVpx04c5WnZm4hIiqaUfdV45l21cnn63PrRtfOw6xeEBkOD/0Xmj7ummKVclOWBoGIdAA+A3yBycaY9zPY5l7gU8APOGeMaWNlTco9GGOYG36cN5bspmB+P6YPbU7r6iXSb3jxKMx4FC5HQq/pUOsR5xerlJuzLAhExBcYDzwARAKbRGSJMWZPqm2KAF8AHYwxx0SklFX1KPdxNS6RVxftYsn2k7SuVoKPezWgVHAGk8Kd2g4zH4OkeBj4HVRq4fxilfIAVh4RNAMijDGHAERkNtAF2JNqm77AQmPMMQBjzFkL61FuYNeJy4wK28Lxi7G88OCdPNGmqm3xmLT+WmVbYD6gMAxdAqVqOr9YpTyET/ab3LbywPFU9yPtj6VWAygqIr+JyGYRGZjRC4nICBEJF5HwqKgoi8pVrmSMYerawzz6xZ/EJ6Uwe0QLnrqvWsYhsGOe7UigSCV4/FcNAaX+JiuPCDLqsmEyeP8mQFsgEFgnIuuNMQdu2cmYScAkgNDQ0LSvodzcpZgEXpy/g1/2nKFdrVJ82KMBRQv4p9/QGPjzf7a1BELuhl4zILCI0+tVytNYGQSRQMVU9ysAJzPY5pwx5hpwTUT+ABoAB1BeYfPRi4yetZWzV+P458O1Gdoq5OaMoamlpMAvr8L6L6BON+j2JeTL7/yClfJAVp4a2gRUF5EqIuIP9AaWpNnmO+BuEcknIkFAc2CvhTWpPOS3/Wfp89V6fH2EBU/cxbDWVTIOgaR4WDDMFgLNn4DuUzQElMpFlh0RGGOSRGQU8DO27qNTjDG7RWSk/fmJxpi9IvITsANIwdbFdJdVNam8Y8XeMzwxYwvVSxdkxrDmGZ8KAoi7DLP7wZHV8MA7cNfTOlBMqVwmxrjXKffQ0FATHh7u6jLU3/DL7tM8FbaFWmULMX1o85uLyad15aRtMZlz+6HrBKjf07mFKuVBRGSzMSY0o+d0ZLFyqp92nWJU2Fbqli/MN0ObUTgwkxCI2g8zukPsReg3D6re79xClfIiOQ4CEfEBChpjrlhQj/JgS3ecZMzsbTSsWIRpQ5oSHJBJCBxbD2G9wNcfhvwIZRs4t1ClvIxDF4tFJExEColIAWwDwvaLyAvWlqY8yXfbTjB61laaVCrKN0ObZR4Ce5fCt10gqLhtjICGgFKWc7TXUG37EUBX4EegEjDAqqKUZ1m4JZKxc7bRrEoxpg1tSsH8mRyIbvoa5g6A0nVh2K9QNMSpdSrlrRwNAj8R8cMWBN8ZYxJJPzhMqXTmhh/nuXnbuatqCaYObkaQfwYhYAysfNe2lkD19jBoCRQo7vxilfJSjgbBl8ARoADwh4hUBvQagcrSrI3HeHH+DlpXK8HkQaEE+vum3yg5EZaMgj8+hEYDoNdM8C+QfjullGUculhsjPkc+DzVQ0dF5D5rSlKeYPr6o/xz8S7uu7MkE/o3IcAvgxBISoC5A22LybR5Ce59SccIKOUCDgWBfbrogUBImn1G535Jyt1NW3uYN7/fQ7tapRjfr/Gtawpfl5xkGy18YJkuJqOUiznaffRHYD2wE9sIYKUyNHn1Id79YS/ta5dmXN/G+OfL4OxjSgoseRr2LoEH39MQUMrFHA2CAGPMs5ZWotzel7//xXvL9tGpXhk+690Iv7RLSoLtwvCyF2F7GNz3KrR80vmFKqVu4ejF4ukiMlxEyopIsetfllam3Mr4VRG8t2wfjzQox+eZhQDAirdg01e2OYPu0aEoSuUFjh4RJAAfAq9ys9uoAe6woijlXj5fcZCPfz1A14bl+OixBukXl7/uj49gzScQOtQ2gZxeGFYqT3A0CJ4FqhljzllZjHIvxhg+WX6Qz1ccpHvjCnzQoz6+Ga0oBrB+Iqx8B+r3gk7/1RBQKg9xNAh2AzFWFqLcizGGj37Zz/hVf9ErtCLvPVov42UlAbbOgJ/+D2o+DF2+AB8rl8FQSuWUo0GQDGwTkVVA/PUHjTHafdQLGWN4/6d9fPn7Ifo2r8S7XepmHgK7F9l6CFVtCz2mgK9OeKtUXuPo/8rF9i/l5YwxvPvDXr5ec5iBLSvzVuc6Ga8qBnDgZ1jwOFRsYVtfWFcVUypPcnRk8TdWF6LyPmMMb32/h2l/HmFIqxBef7h25iFw+A+YY59Aru9s8A9ybrFKKYdlGQQiMtcY01NEdpLBJHPGmPqWVabylJQUw+tLdjFj/TGG312FVzrVyjwEjm+CsN5Q7A4YsAgCCju3WKVUjmR3RDDGfvuw1YWovCslxfDq4p3M2nickW2q8n8d7sw8BE7tgJndIbg0DFwMQTrcRKm8LssgMMacst8edU45Kq9JTjG8tGAH8zZHMuq+ajzXvkbmIXDuIEzvBv7BMPA7CC7j3GKVUrfF0RXKWojIJhGJFpEEEUkWEZ2G2sMlpxhemLedeZsjeaZd9axD4OJR28piIrYQKFLJucUqpW6bo72GxgG9gXlAKLaZSKtZVZRyvaTkFJ6bt53vtp3kuQdq8HTb6plvfOUUfNsZEq7B4B+ghP5qKOVOHO7UbYyJEBFfY0wyMFVE/rSwLuVCickpPDNnGz/sOMWLHe7kyXuz+GC/dh6md4Vr52DgEihT12l1KqVyh6NBECMi/tgGlX0AnMK2WpnyMAlJKYyZvZVlu07zaqdaDL8ni+mk4i7DjG5w8Qj0XwAVmjitTqVU7nF0rP8AwBcYBVwDKgLdrSpKuUZCUgpPhW1h2a7TvP5w7axDIOEazOwJZ/bYBouFtHZeoUqpXOXogLLrvYZigbesK0e5SnxSMk/O2MKKfWd5u0sdBrYMyXzjxDiY3Q8iN8Jj06D6A84qUyllgewGlGU4kOw6HVDmGeISk/nH9M38fiCKf3WrS7/mlTPfODkR5g+FQ6ug6wSo3cV5hSqlLJHdEcGjQGngeJrHKwMnLalIOVVcYjLDvw1nTcQ5/tO9Hr2aZtHtMyUZFj8B+3+ATh9Bw77OK1QpZZnsrhF8AlwxxhxN/YVtSupPrC9PWSkmIYmh0zaxJuIcH/ZokHUIGANLx8LOedD2DWg23HmFKqUsld0RQYgxZkfaB40x4SISYk1JyhmuxdtCYNORC3zcswHdGlXIfGNj4JfXYMs3cPdzcLcuX62UJ8kuCAKyeC4wNwtRzhMdn8SQqRvZcuwSn/ZuROcG5bLe4ff/wLpx0Hwk3P9P5xSplHKa7E4NbRKRdOcARGQYsNmakpSVrsQlMvDrDWw9donPHQmBP8fBb+9Bw/7w4Hu6xKRSHii7I4JngEUi0o+bH/yhgD/QLbsXF5EOwGfYxiBMNsa8n+b5e4HvgMP2hxYaY952sHaVQ5djExk4ZSO7T1xmXN/GdKibzaRw4VPhl1ehdlfo/LkuMamUh8pu9tEzwF0ich9wfe6AH4wxK7N7YRHxBcYDDwCR2I4ulhhj9qTZdLUxRqe5ttilmAQGfL2RfaevMKF/Ex6oXTrrHXbMtV0crv4gPPoV+Pg6p1CllNM5OqBsFbAqh6/dDIgwxhwCEJHZQBcgbRAoi128lkC/yRuIOBvNlwOacH/NbEJg71JYNNI2WrjnN5DP3zmFKqVcwspj/fLcOv4g0v5YWi1FZLuILBOROhbW45XOR8fT56v1RERF89Wg0OxD4K+VMH8IlGsEfWaBn/YJUMrTOTz76G3I6Kpi2lHKW4DKxphoEekELAbSzXcsIiOAEQCVKuk8946KuhpPv8nrOXo+himDmtK6eomsdzi6zjZ1RIka0H8+5A92TqFKKZey8oggEtvkdNdVIM1oZGPMFWNMtP37HwE/EUn3aWWMmWSMCTXGhJYsWdLCkj3H2Stx9J60juMXYpk6xIEQOLkVwnpCoXK2dYYDizqnUKWUy1kZBJuA6iJSxT6FdW9gSeoNRKSM2Je8EpFm9nrOW1iTVzh9OY7ek9Zz6nIc04Y05a6q2YTA2b0w/VEIKGJbXaxgKafUqZTKGyw7NWSMSRKRUcDP2LqPTjHG7BaRkfbnJwI9gCdEJAnbzKa9jTGZTnKnsnfyUix9v1pP1NV4vh3ajNCQbBaPv3AIvu0Kvv4w6DsonMUIY6WURxJ3+9wNDQ014eHhri4jT4q8GEOfr9Zz6Voi3wxrRuNK2ZzeuXwCpnSAhGgYsgxK1XROoUoppxORzcaY0Iyes/JisXKi4xdi6D1pPVfjEpnxeHMaVCyS9Q7RUbbF5uMuwaDvNQSU8mIaBB7g6Plr9Jm0nmsJyYQNb0Hd8oWz3iH2IkzvBldO2C4Ml2volDqVUnmTBoGbO3zOFgLxScmEDW9OnXLZhED8VZjRA87th75zoFIL5xSqlMqzNAjcWMTZaPp+tZ7kFMOsES2oWaZQ1jskxsKsPrauor2mQ9X7nVOoUipP0yBwUwfPXKXPVxsAmDWiBTVKZzP4KykB5g6CI2tscwfVfMgJVSql3IFOJ+mG9p++Su9J6/ERmO1ICKQkw6IRcPBnePgTqP+YcwpVSrkFDQI3s+fkFXpPWkc+X2H2iBZUK1Uw6x1SUmDJaNi9CNr/C0KHOKdQpZTb0FNDbmTXicv0/3oDQX6+hA1vQUiJAlnvYAz89BJsmwFtXoK7RjmnUKWUW9EgcBM7Ii/Rf/IGggP8mD2iBRWLBWW/08p3YOOX0HIU3PuS9UUqpdySnhpyA1uPXaTf5A0UDspBCKz+GFb/F5oMhvbv6hKTSqlM6RFBHrf56AUGTdlE8YL+zBregnJFHFgfYONXsOItqPcYPPSxhoBSKkt6RJCHbTx8gYFfb6RkcH7mjGjpWAhsC4Mfn4c7H4KuE3SJSaVUtjQI8qh1f51n0JSNlCkcwJwRLShTOCD7nXYvhu+egjvug8emgq+f5XUqpdyfnhrKg9ZGnGPYN5uoWDSIsOEtKBmcP/udDvwCCx6HCs2g90zI58A+SimFHhHkOb8fiGLotE2EFC/ArBEOhsDh1TB3AJSuDf3mgn823UqVUioVPSLIQ1btO8s/ZmymWsmCzHi8OcUK+Ge/U+RmmNUbioZA/0UQkM2kc0oplYYGQR6xfM8Znpy5hRplCjJjWHOKBDkQAqd3wYxHoUBJGLAYChS3vE6llOfRU0N5wE+7TvPEzM3UKhvMzGEtHAuBcxEwvSv4BdnWGS5U1vI6lVKeSY8IXOzHnacYPWsr9SoU5puhzSgU4EBPn0vHbKuLGWMLgaKVrS9UKeWxNAhc6PvtJ3lmzjYaVSzC1CFNCXYkBK6ehm86Q8JVGPwDlKxhfaFKKY+mQeAii7ee4Nm52wgNKcbUwU0pkN+BH0XMBfi2K0SftR0JlKlneZ1KKc+nQeAC8zdH8sL87bSoUpyvB4cS5O/AjyHuiu3C8MXD0G8eVGxqfaFKKa+gQeBkczcd5/8W7qBV1RJ8NTCUQH8HpoBIiIGwXnB6J/QOgyr3WF+oUspraBA4UdiGY7yyaCf31CjJpAFNCPBzIASS4mFOPzi+HnpMgRoPWl+oUsqraBA4yfR1R/jnd7u5v2YpvujX2LEQSE6C+UPhr5XQZTzU6WZ9oUopr6NB4ART1x7mre/30K5Wacb3a0T+fA6EQEoKfPck7FsKHT+ARv2tL1Qp5ZU0CCw2efUh3v1hLx3qlOHzPo3wz+fAGD5j4MfnYMccuP+f0Pwf1heqlPJaGgQWmvj7X7y/bB8P1SvLp70b4ufrYAj8+jqET4HWY+Ge560vVCnl1TQILDJu5UE++uUAjzQoxyc9G5DPkRAA+OND+PNzaDoc2r5hbZFKKYUGgSU+XX6AT5cfpFuj8nzYo77jIbDuC1j1L2jQ13ZdQJeYVEo5gQZBLjLG8MmvB/h8ZQQ9mlTgP93r4+vj4If55m/g55ehVmfo/D/w0fkAlVLOoUGQS4wxfPDzfib89he9m1bk393q4eNoCOycD9+PgWoPQPevwVd/LEop59FPnFxgjOG9ZfuY9Mch+jWvxDtd6joeAvuXwaJ/QOVW0Gs65HNgCmqllMpFlp5/EJEOIrJfRCJE5KUstmsqIski0sPKeqxgjOHtpXuY9MchBrWszLtdcxACh36DuYOgbAPoOxv8Ai2tVSmlMmJZEIiILzAe6AjUBvqISO1MtvsP8LNVtVjFGMMbS3Yzde0RhrQK4c3OdRBHL/Ae2wCz+kDxatBvPuQPtrZYpZTKhJVHBM2ACGPMIWNMAjAb6JLBdk8DC4CzFtaS61JSDK8t3sW3644y4p47eP3h2o6HwMltMPMxCC4LAxdDUDErS1VKqSxZGQTlgeOp7kfaH7tBRMoD3YCJWb2QiIwQkXARCY+Kisr1QnMqJcXwyqKdzNxwjCfurcrLHWs6HgJn99mmkw4oZFtToGApa4tVSqlsWBkEGX0ymjT3PwX+zxiTnNULGWMmGWNCjTGhJUuWzK36bktyiuHFBTuYvek4o++vxosP3ul4CFw4bFtn2CefLQSKVLS0VqWUcoSVvYYigdSfdBWAk2m2CQVm2z9ISwCdRCTJGLPYwrpuW3KK4fl521m09QRj29VgTLvqju985aRtneGkOBj8IxSval2hSimVA1YGwSaguohUAU4AvYG+qTcwxlS5/r2ITAOW5tUQSEpOYezc7Xy//STPt6/BqPtzEALRUbYQiLkAg5ZA6XTXzJVSymUsCwJjTJKIjMLWG8gXmGKM2S0iI+3PZ3ldIC9JTE7hmdnb+GHnKV7qWJORbXLw13zsJZjRDS4dhwELoXxjy+pUSqnbYemAMmPMj8CPaR7LMACMMYOtrOV2JSSl8PSsLfy8+wyvPVSLx+++w/Gd46NhZg+I2g99ZkHlu6wrVCmlbpOOLM5CfFIyT83cyvK9Z3jjkdoMaVUl+52uS4yD2X3gxBbo+Q1Ua2ddoUop9TdoEGQiLjGZJ2ZsZtX+KN7pUocBLUMc3zk5EeYNhsOroduXUOsRq8pUSqm/TYMgA3GJyYyYvpk/DkTx72716Nu8kuM7pyTDwhFwYBk8/Ak06GVdoUoplQs0CNKITUhm+LfhrP3rHB90r0/Ppjno65+SYptFdPdCeOAdCB1qXaFKKZVLNAhSiUlIYti0cNYfPs+HPRrQo0kFx3c2Bn5+BbZOh3tehFajrStUKaVykQaBXXR8EkOnbSL8yAU+6dmQro3KZ79Taqv+DRsmQIsn4b5XrClSKaUsoEEAXI1LZMjUTWw9fonPejfikQblcvYCaz+DPz6AxgPhwX/rEpNKKbfi9UFwJS6RQVM2sjPyMv/r04hO9crm7AU2TYZfX4e6PeDhTzUElFJux6uD4HJMIgOnbGDPqSuM79eYB+uUydkLbJ8NPzwHNTpCt4ng42tNoUopZSGvDYJLMQn0/3oDB05HM6FfE9rVLp2zF9izBBY/AVXawGPTwNfPkjqVUspqXhkEF64l0G/yBv6KiubLAU24r2YO1wQ4uBzmD4UKTaF3GPgFWFOoUko5gdcFwbnoePpP3sDhc9f4amAobWrkcH2DI2thTn8oVQv6zoX8Ba0pVCmlnMSrgiDqajx9v1rP8YsxTBnclFbVSuTsBU5shrBeUKQSDFgEgUUsqVMppZzJa4Lg7JU4+ny1npOX4pg6uBktqxbP2Quc2QMzutvWFx64GArkMESUUiqP8pog2HLsEmevxPPN0GY0q5LDxeLP/2VbWCZfgG1hmUI5HGeglFJ5mNcEQYe6ZWhepRhFC/jnbMdLx+GbzmCSYeBSKBpiSX1KKeUqXhMEQM5D4OoZ+LYzxF+Fwd9DyTutKUwppVzIq4IgR2IuwPRutjAYuBjKNnB1RUopZQkNgozEXbFdGD4fAf3mQcVmrq5IKaUso0GQVkIMzOoNp3dArxlwRxtXV6SUUpbSIEgtKR7mDoCjf0L3yXBnR1dXpJRSltMguC45CRY8DhHLofP/oF4PV1eklFJO4ePqAvKElBRYMgr2LoEO79vWFVBKKS+hQWAMLHsBts+C+16DFk+4uiKllHIq7w4CY2D5m7bFZVqNgXued3VFSinldN4dBKs/grWfQugwaPeWri6mlPJK3hsE6yfAynehfm/o9JGGgFLKa3lnEGyZDj+9BLUegS7jwcc7/xmUUgq8MQh2LYTvR0O1dtD9a/DVHrRKKe/mXUGw/ydYOBwqtYSe0yFffldXpJRSLuc9QXD4D5g7EMrUgz6zwT/I1RUppVSe4D1BUKAUhLSC/gshoJCrq1FKqTzD0iAQkQ4isl9EIkTkpQye7yIiO0Rkm4iEi0hry4opVdO2znBQDlcnU0opD2fZlVIR8QXGAw8AkcAmEVlijNmTarMVwBJjjBGR+sBcoKZVNSmllErPyiOCZkCEMeaQMSYBmA10Sb2BMSbaGGPsdwsABqWUUk5lZRCUB46nuh9pf+wWItJNRPYBPwBDM3ohERlhP3UUHhUVZUmxSinlrawMgoyG6qb7i98Ys8gYUxPoCryT0QsZYyYZY0KNMaElS5bM3SqVUsrLWRkEkUDFVPcrACcz29gY8wdQVURKWFiTUkqpNKwMgk1AdRGpIiL+QG9gSeoNRKSaiG2SHxFpDPgD5y2sSSmlVBqW9RoyxiSJyCjgZ8AXmGKM2S0iI+3PTwS6AwNFJBGIBXqlunislFLKCcTdPndDQ0NNeHi4q8tQSim3IiKbjTGhGT7nbkEgIlHA0dvcvQRwLhfLcSVtS97kKW3xlHaAtuW6ysaYDHvbuF0Q/B0iEp5ZIrobbUve5Clt8ZR2gLbFEd4z15BSSqkMaRAopZSX87YgmOTqAnKRtiVv8pS2eEo7QNuSLa+6RqCUUio9bzsiUEoplYYGgVJKeTmPDQIRqSgiq0Rkr4jsFpEx9seLicivInLQflvU1bVmR0QCRGSjiGy3t+Ut++Nu1xawrVUhIltFZKn9vru244iI7Ly+sJL9MXdtSxERmS8i++z/Z1q6W1tE5E77z+L61xURecbd2nGdiIy1/3/fJSKz7J8DlrTFY4MASAKeM8bUAloAT4lIbeAlYIUxpjq2hXHSrZyWB8UD9xtjGgANgQ4i0gL3bAvAGGBvqvvu2g6A+4wxDVP17XbXtnwG/GSfCbgBtp+PW7XFGLPf/rNoCDQBYoBFuFk7AESkPDAaCDXG1MU2TU9vrGqLMcYrvoDvsK2Wth8oa3+sLLDf1bXlsB1BwBaguTu2BdsstCuA+4Gl9sfcrh32Wo8AJdI85nZtAQoBh7F3HnHntqSqvT2w1l3bwc31XIphmxNuqb1NlrTFk48IbhCREKARsAEobYw5BWC/LeXC0hxmP52yDTgL/GqMcde2fAq8CKSkeswd2wG29TV+EZHNIjLC/pg7tuUOIAqYaj9lN1lECuCebbmuNzDL/r3btcMYcwL4CDgGnAIuG2N+waK2eHwQiEhBYAHwjDHmiqvruV3GmGRjO+StADQTkbouLinHRORh4KwxZrOra8klrYwxjYGO2E493uPqgm5TPqAxMMEY0wi4hhucPsmMfdr7zsA8V9dyu+zn/rsAVYByQAER6W/V+3l0EIiIH7YQmGmMWWh/+IyIlLU/XxbbX9huwxhzCfgN6ID7taUV0FlEjmBbw/p+EZmB+7UDAGPMSfvtWWznopvhnm2JBCLtR5kA87EFgzu2BWzBvMUYc8Z+3x3b0Q44bIyJMsYkAguBu7CoLR4bBPYFb74G9hpjPk711BJgkP37QdiuHeRpIlJSRIrYvw/E9kuyDzdrizHmZWNMBWNMCLZD95XGmP64WTsARKSAiARf/x7b+dtduGFbjDGngeMicqf9obbAHtywLXZ9uHlaCNyzHceAFiISZP8sa4vtAr4lbfHYkcUi0hpYDezk5vnoV7BdJ5gLVML2j/2YMeaCS4p0kIjUB77B1nPAB5hrjHlbRIrjZm25TkTuBZ43xjzsju0QkTuwHQWA7dRKmDHmX+7YFgARaQhMxrZK4CFgCPbfNdyoLSIShO0i6x3GmMv2x9z1Z/IW0AtbD8itwONAQSxoi8cGgVJKKcd47KkhpZRSjtEgUEopL6dBoJRSXk6DQCmlvJwGgVJKeTkNAqWyICLJ9pksd4nIPHv3xIy2+9PZtSmVWzQIlMparLHNaFkXSABGpn5SRHwBjDF3uaI4pXKDBoFSjlsNVBORe8W21kUYtgGLiEj09Y1E5EX7OgXbReR9+2NVReQn+wR1q0WkpmuaoFR6+VxdgFLuQETyYZvD5if7Q82AusaYw2m26wh0BZobY2JEpJj9qUnASGPMQRFpDnyBbSpupVxOg0CprAXap/8G2xHB19gm/9qYNgTs2gFTjTExAMaYC/YZcO8C5tmmjQEgv6VVK5UDGgRKZS3WPv33DfYP82uZbC/Y1ilIzQe4lPZ1lMor9BqBUrnrF2Do9d5FIlLMvg7GYRF5zP6YiEgDVxapVGoaBErlImPMT9imCg63n1J63v5UP2CYiGwHdmNbdESpPEFnH1VKKS+nRwRKKeXlNAiUUsrLaRAopZSX0yBQSikvp0GglFJeToNAKaW8nAaBUkp5uf8H+2GBrGfczTMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve = (df_cop[[f\"claim_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                 .melt()\n",
    "                 .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                 .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "factual_claim_curve = (df_cop\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"claim\"])))\n",
    "\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim|price)\")\n",
    "\n",
    "plt.ylabel(\"Claims\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0aa908",
   "metadata": {},
   "source": [
    "### Using IPW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe246c71",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:24.349082Z",
     "start_time": "2023-07-13T18:15:23.790850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve = (df_cop[[f\"claim_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                 .melt()\n",
    "                 .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                 .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "factual_claim_curve = (df_cop\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"claim\"],  weights=1/d[\"ps\"])))\n",
    "\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim|price)\")\n",
    "\n",
    "plt.ylabel(\"Claims\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1d4cec",
   "metadata": {},
   "source": [
    "### Using Orthogonalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "54acc774",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:24.991434Z",
     "start_time": "2023-07-13T18:15:24.350405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kc+YdQW/gkNb6sNY6F/gUGFPkGA0EKWMISx0gDSh5yIuotIKJQIgK05qgY0tpmzePLNUIC/6EW36nQbc2+ARVfITZFfEPgW63we7PjZW6apufXgYUDL6y2fvO4sxE0BRIKvA42b6toLeBjsBxYDfwoC6hMLlSarpSKlYpFZuamuqseIUQVgt88wg+G17EEjmc5LYvkubfj1C9iyY9mmPyctCdbVl9k72ngyUbti1wzLXcxal9sHMx9J4G9Zq7OppCnJkISuoKL/rbvw7YATQBooC3lVLBxZ6k9WytdYzWOqZBgwaOjlMIAUYH7uJbjSGcAx7Ge9KntL1lJCHj/onSefgd/toJFy3hZaJhZ4gYYEwws9WivrUfnwefOjDgEVdHUowzE0EyUDDtNcN451/QXcAX2nAIOAK4T8OZEJ4iPRHmXgdHfoHRbxnDGk0mzD5eeEf0hOZ9YOuH1VcYrs90OHcUDtSS0UNJm2H/N9DfNWWmy+PMRLAFaKuUamnvAB4HFB0gfBQYAqCUagi0Bw47MSanmTJlCuHh4XTp0sXVoQhROcmxMGcIXDgOE76AnpOKHxM9Gc4chMQN1RNT+5EQ3LR2dBrnl5kOh773uDqaEjktEWitLcBM4DtgL/CZ1jpeKTVDKXWpPOYLQH+l1G7gR+DvWuvTzorpkpz0M5zdu4u0XbGc3buLnPSqF0mbPHkyq1fXkncvwnPEL4cPR4JPIExdA60GlXxc55vAr65xV+AQ5dxZmL0gZgoc/slYwrEmO7TGSKCDXFdmujxOnUegtV6ltW6ntW6ttX7Jvu19rfX79q+Pa62Ha627aq27aK0XOTMeMJJARnIitjxjlqQtL5eM5MQqJ4Orr766xCJyQrglreHX1+HzO6Fxd7j7R2jQrvTjvf2h2zjY85UxIcpRyppVGz0ZzD41+67AZoM1z0FIJPS809XRlMrjSkxkpRyDogOTtM3YLoQnsOTCipnw43PQZSxMWgGBFZhjEj0ZrLmw8xOnhwgYMXUZCzs+MTqya6K4pfYy0/90aZnp8nhcIrh0J1DR7ULUKlnp8PFY2L4IBv0dxs4Fb7+KPbdhp+rvNO49HfIyjGRQ01hyYe2LxsJWnW92dTRl8rhEYPIuOSuXtl2I2sBq08xbsZak/wzAcuQ3vm71DEvqTGDjkTROns+mwjXHHNVpXNHrNe0JTWOM5iGbk2c1O9rWD+FsIgx51uVlpsvjcWUi/Rs1JSM5sXDzkDLh36joXDchaodzWXm89eFC7kl5Gh8T/M3/eb7Z35K8PZeXdA3wMRMRGkjLsADjc2ggkWGBRIYF0KCO7+X1KzrfBKsfh9j5EDnAAdFVoPJmn7/AF9Pg8Fq3KNlcITkX4ZdXjPkQbdy//pbHJQLfEGMMb1bKMWx5uZi8ffBv1DR/+5UaP348P/30E6dPn6ZZs2Y899xzTJ061REhC3HFDp68wJJ5r/NY9iyyA5sSNPVL3ghtzatWGyfOZXPkdAYJZzKMz6cz2HfiAt/Hn8Riu/yOPTA/SRiJ4cZGI2m9ZylpJ48TGt64zMWWHKLTjfDdU7Bpds1JBBvfhYxUGPeJW5SZLo/HJQIwkkFVX/iL+uSTGtiGKWq17+JOcPCzf/IP0+dcaNSb4DuXQIAxss3LbKJ5/QCa1w/gagrP1rdYbRw7m5WfHBLOZJJwJoP44+dYHZ/CD7o73/su5v1ZL7LEawwRYQFE2hPFpbuKyNBA6gf6OCZJePkYTVK//AfSDkP9VlU/pxPkXswkI+UMlrTjhK1/A91uJKbmjl1TxFk8MhEIUZvZbJq3foin+fq/M9O8nqyOtxE09m3w8i3/yRhJIiLUeFGnyPIDeVYbyelZnPt0Cfdnriev3T0cSctiV/I5Vu0+QYEbCYL8vIy7iNBAIkMD7E1NRrNTSGXzQ8wUo3zzlrlw3UuVfLLz5WVmkfD9RtIPJNIi70uwZpLWdAKh5SxP6y4kEQhRi1zIzuMfi3/hzwlP0se8j7xBT+E/+FGHNU94m020DAuEAdNh+QyeizqX31eQa7GRnJ5pb2rKtN9NZLA9KZ2Vu44XShIt/LL5BVi67RhJFw7Y7yYCaBkWSL2AEgZuBDeGjqNh+0K45kljApwbyUo9S/qBRHxtqTS0rifV3JvE2DPU6XYBv5Bi5dPcjiQCIWqJw6kXee7Dr3ju4vM090pD3zQX7663OOdinW+E1X8v1Gns42WiVYM6tGpQfPZsjsVKUlpWfnI4efI4xEFSWiaz1h4sNIioXoB3gaamgPy7itZRU6gT/wXs+gxi3GsNK0tOLoG2o7TLnYMNb455jcCWZ8GWVzOq6ksiEKIWWLvvJAs/Wcws9SoBfj6Y71gJLfo474KXZhpvnW/MNC6nkJqvl5k24XVoE25PEpn1IA4eHtaOe3qOIDk9M/8u4sgZo29i85E0lu84ViBJaFb7ReL/7X9581AUkQ3q5Dc1RYYFEOTn7bzvtxyBaRvomPsWeSqIfd73kKtCqNOsIT513bOkRFGSCISowbTWvLPuEEd+nMNs7zkQ0hLviZ9D/ZbOv3j0ZNj8gVFjv//9V3waP28zbcKDaBMeVGxfdp6Vo2mZ+R3X8QfHMTb5ZbL/+JXXd7QudGxooI/RD3Gpw/pS/0RYIHV8nfRSpzWs/y++Pz6HtUEUR/ymkZ1mpX7bFjQb2AMv35oxP0kSgRA1VEaOhUc/207H/W/zmvdyrJGDMN++APzrVU8ABWca95tZuX6I/Lf5ZT/Hz9tMu4ZBtGtoTxL9H4LXP+DdyC1k3XQfiWkZJJw2+iUSThvDYNcfSmXZtpxC5wmr45s/mulygjAeB15pkrDkwsqHYcci6DIW85h3aGs1Yc3JwzvQD5NXzXl5rTmRurmkpCQmTZpESkoKJpOJ6dOn8+CDD7o6LFFLJZ7JYOZHvzE9/VVu8NqI7jER86j/grmam0ei74LlMyBhPbQc6PzrefsbZbJ/exv/rBN0aNSMDo2Kd8Zm5lpIPFO4qSnhTCY/H0jl863JhY4ND/K9nBjCLk+miwgNIMCnlJfIzDT4bBIk/GqU6hj8BCiFlzd4+VVsdJY78chEcDr+D5J+3kru+Qx8ggNpPiiasM6ty39iGby8vHjttdfo2bMnFy5cIDo6mmHDhtGpUycHRS2E4ZcDqTyz+Cfe4N90Nx+EYc+j+j/gmolLlzqNt35YPYkAIGYq/PaWsZLakKdLPCTAx4uOjYPp2Lh4ksjIsdjvIC7fSSScyWDtvlROXyycJBoG+/Lzo9fg511gic4zf8DHt8K5JLhpNnS/3aHfnit4XCI4Hf8HR77dgM1iLIGXez6DI98adVOqkgwaN25M48aNAQgKCqJjx44cO3ZMEoFwiJzMHPJycvl41ymWfbeGT/xeJVydh7ELoNMY1wXm7Q/dxxsvyhXoNHaIkAhod72RfK5+rOJF8+wCfb3o3KQunZvULbbvQnaecSdhTxCnLuQUTgIJ62HJBFAmo2prRL8qfjPuweMSQdLPW/OTwCU2i5Wkn7dW+a7gkoSEBLZv306fPk4ctSE8gs1qIzEuge8X/MgXeQGE1Uliue8s/P0DUeNXQbNoV4dodBpver+Sncb2PoIrvYvpPc1Y+jH+C4j685WdowRBft50aVqXLk2LJwm2fwxfP2h0xP95idvOcL4S7l0Szwlyz2dUantlXbx4kbFjx/LGG28QHOz+E0mEezt55ASzX/yU2Za69KizlQXe/8bmVR+m/uAeSQAgvCM071u95albDYaw9rDpA+df89LiMl/dCxH9jZ99LUoC4IGJwCe45BmJpW2vjLy8PMaOHcsdd9zBzTe7d/1xUTOs2Z7EFxGdGBWwnpd95nLkfHPm7L6JC9YQV4dWWPRkOHPIaDqpDkoZdwUndhhrLjtLbiYsnWyUt4ieDBOWVd+orGrkcYmg+aBoTF7mQttMXmaaD6rauyutNVOnTqVjx4488sgjVTqXEFpr5q0/wjPbztDYms7jXp9y+HxzlhwajVedEMyljWZxlc43OnhN4wroPg58gpy3lOWFk8Z6zntWwPCXYNQb1T8qq5p4XCII69yaltdflX8H4BMcSMvrr6py/8CGDRtYuHAha9euJSoqiqioKFatWuWIkIWHyc6z8tfPd/L8yj0MahXCG16f4GvK5fukQWhMDJ0ygsC67lVrJ7/TeO+Kiq1p7IjmHN8g6HEHxH9pvGg7UkoczBkCqftg3MfQv5LzJGoYN3tbUT3COrd2WMfwJQMGDKj4Kk9ClOL42SxmLNrKruRzPDy0Hfd3taLejyWt8WiievyZJm2b0ritmy6idEWdxlXUa5pxza0fwuC/O+acB76HpXcZieaub6FJlGPO68Y8MhEI4Y62JKRxz6KtZOfZ+N+kGIZ1aggf3wY+gYTe8TqhFVlg3pUKdhpXdqbxlQprA62HGMNXBz5S9aabTR8YK7A17GKMDApu4pg43ZzHNQ0J4W601izamMj42RsJ9vNm+X1XGUngj7Vw8Du4+m/g7kngkuruNAZjKcuLKUaz1JWyWuCbv8G3jxlzFO761mOSAEgiEMKlcixWnvhiN/9YHsfV7RqwfOZVRoVOm9VYnrFeBPSZ4eowKy6/03h+OQc6sBm1zVAIiYTN/7uy52efh09uhy3/M5q0bl8IvjWjaqijSCIQwkVOns9m3OyNfLoliZnXtGHOpBiCL5VS3rYATu2BYc9XeGUxt5Dfafw1ZJwu/3hHNB+ZzEZfwdHf4cSuyj337FGYdx0c/glueBOGv2icz8M4NREopUYopfYrpQ4ppR4vYf+jSqkd9o84pZRVKVXfmTEJ4QwZFzNJSjzOuXPnK3T8tqPp3PDWevanXODdO3ryt+vaYzLZXxSzz8O6l6BFP9eWj7hS0ZPBmgs7q3Ed7x53gHeAURa7opK2wP+uhXPHjPkB0ZOdFp67c1oiUEqZgXeA64FOwHilVKHCO1rr/2ito7TWUcATwM9a6zRnxSSEM+yLP8TMux5n5NXjmXr7Q2yP3V3m8Uu2HGXcBxvx8zbzxb39+VPXxoUPWP9fyEg11uatiUMWXTHT2D8Eut0Gu5calUHLE7fMmCPgEwh3/2DMVPZgzrwj6A0c0lof1lrnAp8CZb29GQ9U41sIx8rOzqZ37950796dzp0788wzz7g6JFEN0s6k8+h9z7B1805Ac2DvH9w3+e8cTThW7Nhci41/Lo/j78t206dVfVbMvKp4CeX0RPj9Heh2OzR1kxISVyLmrrI7jZ2RIHpPB0u20axWGq3hl//A0inQpAfcvRYatHd8LDWMMxNBUyCpwONk+7ZilFIBwAhgWSn7pyulYpVSsampqVUOLO6nnbw15TVeHP00b015jbifdlb5nL6+vqxdu5adO3eyY8cOVq9ezcaNG6t8XuHejiWlkHgkmeiGF/lpXDwdQzO5eCGDpMTCieD0xRwmzNnEwo2J/OXqVsyf3KvkRdp/fM6obFlKeeUao9MY8KtXgU5jB97xNOwMEQNgy1yjs70oSw4svwfWvmgk2jtXVE+11BrAmYmgpN9waW8DbgA2lNYspLWerbWO0VrHNGjQoEpBxf20k2/eXsG51HOg4VzqOb55e0WVk4FSijp1jJEGeXl55OXloWribb2olDpBATQPsfHaNQnU97fQODAXgOC6l5dd3JV8lhveWs+uY2eZNb4HT/ypI17mEv71kjYbTRb974e6zarrW3COynYaO0qf6XDuKBxYXXh7xhlYcKPRb3HNU3DTBzWrE97JnJkIkoHmBR43A46Xcuw4qqlZaN2CNeTl5BXalpeTx7oFa6p8bqvVSlRUFOHh4QwbNkzKUHuAFk1CWHhrOnV9LYDRpD9+8s20ahMBwLKtydzy/u+YlGLZPf0Z3b2Uselaw3dPQp1GcFUtWdku+k6j03jH4uq7ZvuRENwU62/vcS71LBaLBU4fNMpFHNsKY+fCoMdqZt+LEzkzEWwB2iqlWiqlfDBe7IvN+FBK1QUGAV85MZZ8506fq9T2yjCbzezYsYPk5GQ2b95MXFxclc8p3JjWmFc+QEheMikdjbH+Mx6azD0P3YWvvx/Pf72Hv36+k+gWIXx9/4ASF0LJF7cMkrfAkH/WnjHsZXYaO6cTWSsTaU1vxHz0V5Y88Ayb//tvbLOvhZwLMHkldL3FKdet6ZyWCLTWFmAm8B2wF/hMax2vlJqhlCo4Q+Ym4HuttWMWBChH3bCS/xlL234l6tWrx+DBg1m9enX5B4uaa/3rEP8lathzNL1mIgDtO7TC5uPHpHmbmbfhCHddFcmCqb2pH1hCf8AleVmw5llo1NVoTqlNYu6CtD+MtX1L4uB35qcST7HgcysWm5lRzVbR5+KrXMirQ96d30Pz3g69Vm3i1HkEWutVWut2WuvWWuuX7Nve11q/X+CYD7XW45wZR0HXTBqKt2/heiTevt5cM2lolc6bmprK2bNnAcjKymLNmjV06NChSucUbmz/t/DjC9D1ViiwXnBSWgY3vLWe2MR0Xr21O8/c0BnvkvoDCtr4rrH+7XX/V/smM+V3Gn9YLZc7nZTKxRxf4tPa0STwFAnnmzF7+xjOWx33Rq828riic10GdweMvoJzp89RN6wu10wamr/9Sp04cYI777wTq9WKzWbjtttuY9SoUY4IWbib1P2wbBo07g6j3yr0rvbV7/Zj9W/I53/pR/fm9co/18VT8OvrRtt2y6udF7Or5K9pPNfoNHZyzSTfQGP94rXHriLpYhN2numEf90gvH3LuCMTnpcIwEgGVX3hL6pbt25s377doecUbigrHT4ZZ7zAjVsM3v5YbcYiMtOAFqEB/GPqABoEVXBEytoXjbHvw553atguFT0ZNr1ndBpf9YCxzUkTzRq1bERE15Yk7j7CjjNdABg+7XqCQ2XZ2LJ4ZCIQ4opYLcZEpLNJRsdj3aaczczl/k+2c/JQMtN84YFr2+Jd0SSQEgfbFxpF5cLaODd2VwrvYJTL2PqhMTS2UL+AY/sI6tQPYswjYzlx6DhZFzMJbRJKo9aeU0X0SkkiEKKifnzWKA19wyxo0Zf9KReYvjCW42ezeHNoO/gVvCva66Y1fP8U+AbD1Y86M2r3ED0ZvvyL0Wns5Caw4LBggsPkDqAypPqoEBWx81P47S2jjEH0nayOO8FN724gK9fKp9P7Fa8XVJ6D3xsVLwc/AQEeUGexmjuNReVIIhCiPMe2wooHIHIgtmEv8dr3+5mxaBvtGgbx9f0DiI4I4XITRwXavq158P0/ILQN9JrqzMjdx6VO4z0r7DONZVlXdyKJQIiyXEiBT++AoIZcGD2HaR/v5K21h7gtphlL/tKXhsHGKJVKjYff+iGcPmDUvq/q0oo1SfRksOUVnmksM3zdgvQRCFEaSw4smQjZ5zh601dMnr+Po2cyeWFMZyb0jSi5llR5o2Gy0mHd/xnt5O1GOCdud1Ww07jLWFdHIwqQOwIHs1qt9OjRQ+YQ1HRaw8pHIHkzu3r9mz8tSed8Vh4f392Hif0iS0gCFXxn+8urRjIYXkPXGqiq6Mn2mcbVuKaxKJdHJoJvlv/Adf1vo3vkYK7rfxvfLP/BYed+88036dixo8POJ1xk82zYsYhNLaYxem0oLcMCWTFzAH1aVaFs8Zk/YNMH0GMCNO7muFhrEuk0dkselwi+Wf4Dzz3+H04cO4nWmhPHTvLc4/9xSDJITk7mm2++4e6773ZApMJlDv+EXv0EOwL6M+7AIG7u0ZTPZ/SjST3/0p9z6d19WU1Da54Bsw9c+w/HxluTXOo0PvqbfYMH3hW5IY9LBLNe+R/ZWTmFtmVn5TDrlf9V+dwPPfQQr7zyCiaTx/1Ya4+0I1iXTCZBNWXS2bv556guvHZbd/y8q1gDKGGDUZt/4MMQ1MgxsdZQqRE35H+9ZvUvHDpwxIXRCPDARJBy/FSltlfUypUrCQ8PJzq6Bi8v6OlyLnDxo9u4mJPLA/pR3psyiCkDWlZwgaEyho/abMZaA8HNoN9MR0Zc4+Tl5vHBx5vYlhIIwPp1m7j3zsc4npzi4sg8m8clgkZNwiu1vaI2bNjAihUriIyMZNy4caxdu5YJEyZU6Zyi+miblcP/m4j/2YO8XOcJ3r3/Fq5q46ACabuWwIkdMPRZo2nEgyUnHWfp4q/5fP/lvpaU46fkrsDFPC4RPPDYNPz8C9eC8fP35YHHplXpvP/6179ITk4mISGBTz/9lGuvvZZFixZV6ZyiemTmWlj1ziO0Or2OL8Pv5Z8P3EPz+gGVO0lpfQS5GfDj88ZC9DJkEoXCZFL8kFiPJXtD2ZJiLMJj8sQRVG7E4xLByBuH8czLj9K4aUOUUjRu2pBnXn6UkTcOc3VowgWS0jJ57Y1XGXnmQ/Y1uoGx97xIgE9VptcUSQS/vQUXjtvXGvC4f7dimrZozPjJN5NrNfHSxuYkXfClRWQz2rRv5erQPJpHTigbeeMwp77wDx48mMGDBzvt/MIxNhw6zX8//pIF+nXOh3anw9Q5jn2xPn8cNrwJnW+CFn0dd94azNvbm8nTx9O+Uxt+XP0rXaM6cu11A6vcNCuqptKJQCllAuporc87IR4hnE5rzdz1R3h31Wa+8fsPPoEhBEz+DLz9rvykJTUN/fgC2CxG34DI16BhKKPHjmD0WA+bWe3GKvT2Rym1WCkVrJQKBPYA+5VSHlA7V9Q22XlWHvlsJy9/s5vF9d6nkeksXuMXO35I5/HtsHMx9L0XQiIde24hHKyi98Gd7HcANwKrgBbARGcFdSW0k1Y8Es7hit/XHwmnuPG/61i+/RhLmn1Fh6ztqBvehGaOGPJbYPio1vDdPyAgDAY+4oBzC+FcFU0E3kopb4xE8JXWOg83qiPr5+fHmTNnJBnUEFprzpw5g59fFZpiKumXnUe5efZmEtOymOu/iujTy8hqPwmixjv+YvtWQuJ6uOZJ8JNF04X7q2gfwQdAArAT+EUpFQG4TR9Bs2bNSE5OJjU11dWhiAry8/OjWbNmTr+O1poFvyfy/NfxNFdZvBq4jp7WTzlnas+R9KvonJmNd4ADEtKlPgJLDvz8CjToCD3vrPp5hagGFUoEWutZwKwCmxKVUtc4J6TK8/b2pmXLlq4OQ7iZ7DwrTy/fzZ5tv/J+8AYG5/yEtzWLDNWEg96TINuCzWp1yLVsFismQG/+Hyr9CExYBmaPHJQnaqAK/aUqpeoBk4DIIs95wPEhCVF1J1OOs2LhG9x14Vs6+h5FW3w5Y+5Cqqkv502tQZlo3LMDPnUqOXGsBJasTDKTEwgG1Kl4bJGDUa2HSDk1UWNU9C3LKmAjsBuwVfTkSqkRwJuAGZijtX65hGMGA28A3sBprfWgip5fiEJsVji8jrT18whJ+I5pWDgX2gX6vwadx+J9Kgvrz1vxPp9BeI8ONOjapoJ1hMq4pMVCRtIRdE42AFqZON9mEnWys/Dyr3qSEaI6VDQR+GmtKzX8QSllBt4BhgHJwBal1Aqt9Z4Cx9QD3gVGaK2PKqVkVomovLQjxvKHOxbD+WTQdVjhPYKYmx8gslMfwBjTUzcyhMDGYWiLBa8A/yonAQBbXi7W7CyUMv6VclqOwhYcgTU3RxKBqDEqmggWKqWmASuB/BrOWuu0Mp7TGziktT4MoJT6FBiDMQ/hkj8DX2itj9rPV7USoMJz5GUZZZ23L4Qjv6BRHAjqzZu5N5PT+jpeH9+HugHF1wP28vUBXx+HhaHMZpTZjPYP5UL//yMvrDsAJukfEDVIRf9ac4H/AE9xedioBsoqENIUSCrwOBnoU+SYdhhDU38CgoA3tdYLKhiT8DRaGxO1ti+C3Ush5xzUi+DiVX/nr/s7812yFzMGtebR69pjNlVPC73Zx5eAphFkHD1MXiPjz9u3fgPM/p5dZVTULBVNBI8AbbTWpytx7pL+E4sO9PcCooEhgD/wu1Jqo9b6QKETKTUdmA7QokWLSoQgaoWMM7D7MyMBnIwDLz9jycMeE9hh7sKMRds5m5XLW+O7c0P3JtUenk9wPcxtOmLNzcHk5YXZL0DuCESNUtG/1nggs5LnTgaaF3jcDDhewjGntdYZQIZS6hegO1AoEWitZwOzAWJiYmTWmCewWeGPdUbTz/5VYM2FJj1g5OtGOWf/eqzYeZy/fb6J8CBfvrjnKjo1CXZJqMpkwisgEK+AQJdcX4iqqmgisAI7lFLrKNxHUNbw0S1AW6VUS+AYMA6jT6Cgr4C3ldHT5oPRdPTfCsYkarALaRewWqwEhwZhMhdYBjLtCOz42N7xewz860OvuyHqDmjUJf+w7+NTeHjJDqIjQnh/QjT1Ax3X7i+Ep6loIlhu/6gwrbVFKTUT+A5j+Og8rXW8UmqGff/7Wuu9SqnVwC6MYalztNZxlbmOqFlys3PZ//se1sz7juyL2fS8vhd9RvWk3ulfYNsCSPgVUNBmiFHDv/314FV4IaENh04zc/F2ujaty/zJvQj0lWYYIapC1bT6PDExMTo2NtbVYYgrlLD7CIuenA9oGgecIiosnu4N/8DLlgn1IqDHRKP+T92Sy09sP5rOHXM20TwkgCV/6Uu9ALkTEKIilFJbtdYxJe0r862UUuozrfVtSqndlFBkTmvdzUExCg9xbJ8xkGxg400MarKJPJuZQxc6EXHn0/h3GlrmwjD7Uy4wef4WGgT5snBqb0kCQjhIeffUD9o/j3J2IMIzBIUG4W3Ko0/4dg6ei2T5kRE06tSBVm0Gl5kEEs9kMGHuJvy8TSya2ofw4OqrXCpEbVdmItBan7B/TqyecERt16xjBL1bHcfPK5ffUmKwqAAG3XENPn6lv7tPOZfNhLmbsFhtfPaXfpVfWF4IUaaKFp3rC7wFdMQY3WMGMrTWrhmvJ2qs+o1CGNj6INnZreg5/X6ui2xIw8jSVwdLz8hl4txNpGfksXhaH9o2DKrGaIXwDBUdbvE2xvDPz4EYjEqkbZwVlKjFjm3F63Q8XiNfo2uvqDIPvZCdx53zN5OYlslHd/WmW7N61RKiEJ6mwuPutNaHlFJmrbUVmK+U+s2JcYnaastc8KkD3W4v87DsPCvTFsSy5/h5PpgYTb/WodUUoBCep6KJIFMp5YMxqewV4AQg0yhF5WSmQdwy6DkRfEtv4smz2pi5eBubjqTxxu1RDOnYsBqDFMLzVHTN4okY/QIzgQyM0hFjnRWUcL1jSSfYtX0Px5JTHHfS7YvAmgMxU0s9xGbT/O3znazZe4rnx3RhTFRTx11fCFGiii5VeWnUUBbwnPPCEa6mtebXdRt58qGXOH/uAsF1g3h51j8ZMLho4dhKstkgdi5EXAUNO5V67WdWxPPVjuM8el17JvaNqNo1hRAVUt6EshInkl0iE8pqn6TEYzx633NkZWZRz9fC2XMXePS+Z/ls1RyaR1Th3fkfayE9AYY8Xeohr36/n4UbE/nLoFbcO7j1lV9LCFEp5d0R3Aw0pPC6AgARFK8kKmqBlOOpZGVm0avRBeZe/wc3fdmeP87CyROpVUsEW+ZAYDh0uKHE3R/8/AfvrPuD8b1b8PiIDg5ZPUwIUTHl9RH8FzivtU4s+IFRklqqhNZCoQ1C8Pbx5sa2xuJz9Xyt+Pj6UD8s5MpPmp4IB1ZDz0ngVXzi2Cebj/Kvb/cxqltjXryxiyQBIapZeYkgUmu9q+hGrXUsEOmUiIRLRbRsxrMv3s+1EecAMJlMPPOvvxHRsuQicBWy9UNQCqInF9v19c7jPPnlbga3b8Drt0VV28piQojLymsaKqugi6zFVwt5eXlxfYc8vPbaAHj2lUdp1G8I5oJrBlSGJccoL93ueqjXvNCudftP8fCSHfSKqM97d0Tj41XRQWxCCEcq7z9vi33R+kKUUlOBrc4JSbia194v879u3qIJ3t5VqPe/ZwVknoZehYeMbj6Sxj2LttKhcRBzJsfg73OFiUYIUWXl/Yc/BHyplLqDyy/8MRj1hm5yYlzCVbLOwsHvoWFXOLnbWDC+KrbMgfqtoNU1+Zvijp1j6odbaFrPn4/u6k2wn3fVriGEqJLyqo+eBPorpa4BLq0T+I3Weq3TIxOusW+lsT5w11uMRFAVKXGQtBGGv5RfYvrQqYtMmreZYH9vFk7tQ2gd33JOIoRwtopOKFsHrHNyLMId7P4cQlpC0+iqnyt2Lnj5QZSxVHVyeiYT527CpBSL7u5Dk3rSzSSEO5DeOXHZhZNw5BfjbiB/COcVNg1ln4edS6DLLRBQn9QLOUycu5mMHAsLpvSmZZiUqhLCXUgiEJfFfwnaZrx4V9WuJZCXAb2mci4rj0nzNpNyLpv5d/WiUxNZxkIIdyKJQFwWt9ToJA7vULXzaG10EjfpSWaDbkz5cAuHTl1g9qRooiPqOyZWIYTDSCIQhrQjkLwFul4qKmtvGrqSUUOJGyB1H3k97+IvC7ey/Wg6s8b1YGDbBg4LVwjhOFUYIC5qlbhlxucuDqguvmUO2q8ef93Tml8PnuaVW7pxfdfGVT+vEMIp5I5AGHYvheZ9oV6Lqp3nQgp679f8GjicFXvO8s9Rnbgtpnn5zxNCuIwkAgEn4yF1rzFa6JIrHDWkt36Esll4+ngfHhjSlqkDWjouTiGEUzg1ESilRiil9iulDimlHi9h/2Cl1Dml1A77R+nF6oXz7P4clBk6V3GyuNVCxm9z+MXalcH9+vHw0LaOiU8I4VRO6yNQSpmBd4BhQDJG3aIVWus9RQ79VWs9yllxiHJoDbuXQetrIDCs5P0VtHbFR1ybe4pDkQ/y9KhOUk5aiBrCmXcEvYFDWuvDWutc4FNgjBOvJ65E0mY4d7SEuQOVexFftjUZr23zSTOHM2nSdExSTlqIGsOZiaAphVc2S7ZvK6qfUmqnUupbpVTnkk6klJqulIpVSsWmpqY6I1bPtftzowxEh5FXfIrv4lN4d9l3XG3eTdCAaXh5F198RgjhvpyZCEp6S1i0nWEbEKG17g68BSwv6URa69la6xitdUyDBjIW3WGsFmM2cbvrwK+02b5lNw1tOHSa+xdv54G6v6BN3njH3On4OIUQTuXMRJAMFBw32Iwi6xxrrc9rrS/av14FeCulSmioFk5x5CdjrYCutxbfV4H2/e1H05m2IJYOoWZusK1DdRoNQQ0dH6cQwqmcmQi2AG2VUi2VUj7AOGBFwQOUUo2UvUdRKdXbHs8ZJ8YkCtq9DHyDoc2wSj91X8p5Js/fQoMgXxb1TcaUcw5ippb/RCGE23HaqCGttUUpNRP4DjAD87TW8UqpGfb97wO3APcopSxAFjBO66quhCIqJC8L9n4NncaAdxkrkpbw60g8k8HEuZvx8zaxaEpvgpc+Aw06QkR/JwYshHAWp5aYsDf3rCqy7f0CX78NvO3MGEQpDn4PuRcK1BYqquSmoZRz2dwxZxMWq43P/tKP5ln74MRO+NOrFWpOEkK4H5lZ7Kl2fw6B4RB5dYWfkpaRy8S5mzibmcdHU3rTtmGQUWXUpw50u92JwQohnEkSgSfKPgcHvocuN4O5lJvCIiUmLmTnMXn+Zo6mZTLnzhi6NasHmWlGsbput5cx6kgI4e4kEXiivSvBmlPhBWiy86zc/VEse46f5907etK3VaixY/si4zy9pJNYiJpMylB7orilUC8CmsWUe6jFauO+j7exOSGNN26PYkhH+/BQmw1i50GL/tCwxHmAQogaQu4IPM3FU3D4pyLrEpfE2Pe/Xw/z475TvDCmC2OiCkwMP7wW0o/I3YAQtYAkAk8Tv9xYl7ikSWQFaHvfwMbDaTw2oj0T+kYUPmDLXAhsAB1HOylQIUR1kUTgaXZ/DuGdIbxjmYd9vCkRgD91bci9g9sU3nn2KBxYDT3vBC+pKyRETSeJwJOkJ0Dy5sIL0JTgg5//YOlWoxrIbdElrC629UPjc/Rkh4YnhHANSQSepALrEn+y+Sj/+nYfV7UxRgYVW1PAkgPbFkC7EVBPlqAUojaQROBJdi+D5n0gJKLE3V/vPM6TX+7mmvYNeGhoO2Nj0RITe7+GjFTpJBaiFpFE4ClO7oFT8aXOHVi37xQPL9lBr4j6vHtHNN5mc8nn2TIHQlpCq2udGKwQojpJIvAUcUvt6xLfWGzX5iNpzFi0lQ6Ng5gzOQZ/n4JJoMAdQUocHP3duBswyZ+OELWF/Dd7Aq1h91JoNQjqhBfaFXfsHFM/3EKzEH8+uqs3wX7epZ8ndq6xmlnUHU4OWAhRnSQReILkWDibWGzuwKFTF5k0bzPB/t4sursPoXV8L+8sOtcs+zzsXGJ0NAfUd37MQohqI4nAE+z+HMy+0GFU/qbk9Ewmzt2ESSkW3d2HxnX9S37upc7iXUsgL0M6iYWohSQR1HYlrEuceiGHCXM2kZFjYeHU3rQMCyz7HFobM4mb9ICm0dUQtBCiOkkiqO0SfoGMU/mTyM5l5jFp3mZOns9h/l296di4tPLRBdqGEn+D1L3Q627nxyuEqHaSCGq7S+sStx1OZq6Fuz7czB+nLjJ7UjTRESEVOIE2hoz61YXONzs9XCFE9ZNEUJvlZcPeFdBhFDnKh78s3MqOpLPMGh/FwLYNKnaOiyeNc0RNAJ8A58YrhHAJWY+gNjv4PeScx9p5LA99uoNfD57mlVu6MaJL4/Kfe6m0xLYFYLNAzBTnxiqEcBm5I6jN4paiAxvw5I4Qvo1L4Z+jOnFbTMXqA2mbzfji2FZodQ2EtSn7CUKIGksSQW2VfR69fzVbAgexZFsKDw5py9QBLSv0VEtWJpknj+c/tnaf4KwohRBuQBJBbbXvG5Q1h5eTujC5fyQPDW1boafZLHlcPHoYy8ULAFj9G3DeFIk1N9uZ0QohXEj6CGqp5F8XoG0NaBl1DU+P6lS8nHQprDk52HKyMSuj3lBOy1FobWw3+/g5M2QhhIvIHUEttPK3nTQ6vZG4+kP59y3dMJkqlgQAlNkMKKx1W5IR9SDZbYy1C0ymUqqRCiFqPKcmAqXUCKXUfqXUIaXU42Uc10spZVVKlb10lijXd/EpbFk1Hy9lY8ht9+Flrtyv2Ozji1/DxqBM5LQaDV7++ISEYfIrpQSFEKLGc1rTkFLKDLwDDAOSgS1KqRVa6z0lHPdv4DtnxeIpNhw6zf2Lt/NVwCZsdTvg06Rrpc+hTCb8wsLxDqiDNTcHk7cPZv8ATKWtTyCEqPGceUfQGziktT6stc4FPgXGlHDc/cAy4JQTY6n1th1NZ9qCWPqGZtAxbw+mbreW/6RSmMxeeAcF4xfaAJ/gupi9yyhNLYSo8ZyZCJoCSQUeJ9u35VNKNQVuAt4v60RKqelKqVilVGxqaqrDA63p9qWc5675W2gQ5Mu73Y8YG8tZoF4IIS5xZiIoqYeyyAK4vAH8XWttLetEWuvZWusYrXVMgwYVLI3gIRJOZzBx7mb8vc0smtqHOgeWQ7NeEBLp6tCEEDWEM4ePJgMFp7E2A44XOSYG+NQ+tDEM+JNSyqK1Xu7EuGqNlHPZTJi7CYvVxicz+tHckggn4+D6V1wdmhCiBnFmItgCtFVKtQSOAeOAPxc8QGudP9VVKfUhsFKSQMWkZeQyYe4mzmbmsXhaH9qEB8GPS0GZoPNNrg5PCFGDOC0RaK0tSqmZGKOBzMA8rXW8UmqGfX+Z/QKidBey85g8fzNJaZl8NKU33ZrVMxaPiVsKLYuvSyyEEGVx6sxirfUqYFWRbSUmAK31ZGfGUltk51m5+6NY9hw/z+xJ0fRtFWrsOLYV0hPg6kddGp8QouaREhM1SJ7Vxn0fb2NzQhpv3B7FtR0aXt65e6mxLnHHG1wXoBCiRpISEzWEzab52+c7+XHfKV4Y04UxUQVG4tqsEP8FtB1mrCQmhBCVIImgBtBa8/SKOL7acZzHRrRnQt+Iwgck/GqsJNb1yieRCSE8lzQN1QD/+W4/izYeZcag1tw7+PICMZbsHLLOnMP39wV4+9RBtbvOhVEKIWoqSQRu7v2f/+Ddn/7gz31a8PcR7fO3517IJPHHTaTvPUTPnG9I8+uBX3oWgeFSHE4IUTnSNOTGFm86ysvf7uOG7k14YUyXQmsKXDx2irR9CYTYduFFNqnWbpzYuBubpcxJ2kIIUYzcEbipr3ce56nlu7mmfQNev6075ktrCmgNp/Zi3jybLjnfE6iPkUM9zpva4Z2UgjU3F5OX3BUIISpOEoEbWrfvFA8v2UGvyPq8e0c03gpI2gL7voa9X0PaYYJRXFSRJHqN4Yy5B1qZqdu6GWZfX1eHL4SoYSQRuJlNh88wY9FWOjfy58NBmfj/8Bjs+wYunACTF7S8GvrNxNJiCGd2n+RkrLG8Q0DDUBrFdMZUyYVohBBCEoEbiU88yccf/Y9ZflsZlrkN06dnwcsf2g6FDjdAu+HgHwKAN9A8tBkNurXFZrHiFxKEt7+sKSyEqDxJBK6WfQ4OfI/eu4LWe79jlsrBZq6Lqd31xizh1teCT0CJTzV7eREYXr+aAxZC1DaSCFzh4imjuWffSjj8M9jyUHUaYus+npSI62jUfRiYZVUwIUT1kERQXdITjRf+vV/D0Y2AhpCW0HcGdBwNTWMIMJko+b2/EEI4jySCKtI2G8cOHiPup13kZubQ9ZruNOvYAm8fL0jdZ7zw7/0aUnYZT2jYBQY/Dh1GQcPOoEpayE0IIaqPJIIqOn7wGAsen2efyKU5vXE1N43yJSR9A6T9ASho3huGvQAdR0H9Vq4OWQghCpFEUEUHtxzAy5ZJv8bb6B62h2Cfi9gOmbC1GoSp333QYSQENXJ1mEIIUSpJBFVhyaVJ+kru6/IJgd5ZHDjbkrXHriKr0UBuHT8Dk490+Aoh3J8kgiuhNcR/CT8+T7v0IyRkN+fTQ1dxItNYKObWe4biJUlACFFDSCKorCO/wg9Pw/FtEN4Z2/glmPLaU//bWAIysokZ2ZuIrpGujlIIISpMEkFFndoLPzwDB7+D4KYw5l3oPg6TyUwLoEWXlmitC1UIFUKImkASQXnOH4d1L8GOxeATBEOfhT4zwLt4hU9JAkKImkgSQWmyz8H6N2Dje6Ct0PdeGPhXCJCSDkKI2kUSQVGWXIidCz+/Allp0PU2uPYfEBJR/nOFEKIGkkRwic0G8V/A2hcgPQFaDoJhz0OTKFdHJoQQTuXU4vVKqRFKqf1KqUNKqcdL2D9GKbVLKbVDKRWrlBrgzHhKdeQXmHMtLJtq9ANMWAaTvpIkIITwCE67I1BKmYF3gGFAMrBFKbVCa72nwGE/Aiu01lop1Q34DOjgjHhsNhvZWTkEBBbo5D0Zb4wEOvQDBDeDmz4wmoJMsriLEMJzOLNpqDdwSGt9GEAp9SkwBshPBFrriwWODwS0MwI5uP8wny/6iq2bdjHk+qsZM7wrTQ/OM0YC+QUbdYB6TwdvWdhFCOF5nJkImgJJBR4nA32KHqSUugn4FxAOjHR0ECnHT3H/lCc4npxCkI8Fn1/X0yD9DNrbjOo/EwY8IiOBhBAezZmJoKRB9cXe8WutvwS+VEpdDbwADC12IqWmA9MBWrRoUakgjvyRyPHkFAY0O8//DUwk2NfKN3+E0PGBBbTpfXWlziWEELWRMxvDk4HmBR43A46XdrDW+hegtVIqrIR9s7XWMVrrmAYNGlQqCC9vI9clnvNlV2ogt69oxz83tIS6zct5phBCeAZnJoItQFulVEullA8wDlhR8AClVBtln46rlOoJ+ABnHBlE67aR9OjVjaQLvsxc04r9aQHcNvFGmkc2deRlhBCixnJa05DW2qKUmgl8B5iBeVrreKXUDPv+94GxwCSlVB6QBdyutXZoh3H90BD+7/Un2fTbNvbs2kdMvx7E9OmOr6+PIy8jhBA1lnLw667TxcTE6NjYWFeHIYQQNYpSaqvWOqakfTJgXgghPJwkAiGE8HCSCIQQwsNJIhBCCA8niUAIITycJAIhhPBwNW74qFIqFUh0wKnCgNMOOI+jSVyVI3FVjsRVObUprgitdYmlGWpcInAUpVRsaWNqXUniqhyJq3IkrsrxlLikaUgIITycJAIhhPBwnpwIZrs6gFJIXJUjcVWOxFU5HhGXx/YRCCGEMHjyHYEQQggkEQghhMfziESglJqnlDqllIorsK2+UuoHpdRB++eQao6puVJqnVJqr1IqXin1oJvE5aeU2qyU2mmP6zl3iKtAfGal1Hal1Eo3iytBKbVbKbVDKRXrLrEppeoppZYqpfbZ/9b6uToupVR7+8/p0sd5pdRDro7LHtvD9r/7OKXUJ/b/B3eI60F7TPFKqYfs2xwWl0ckAuBDYESRbY8DP2qt2wI/2h9XJwvwV611R6AvcJ9SqpMbxJUDXKu17g5EASOUUn3dIK5LHgT2FnjsLnEBXKO1jiowvtsdYnsTWK217gB0x/jZuTQurfV++88pCogGMoEvXR2XUqop8AAQo7XugrGg1jg3iKsLMA3ojfE7HKWUauvQuLTWHvEBRAJxBR7vBxrbv24M7HdxfF8Bw9wpLiAA2Ab0cYe4MNa9/hG4FljpTr9HIAEIK7LNpbEBwcAR7INC3CWuIrEMBza4Q1xAUyAJqI+xeuNKe3yujutWYE6Bx/8EHnNkXJ5yR1CShlrrEwD2z+GuCkQpFQn0ADa5Q1z25pcdwCngB621W8QFvIHxD2ArsM0d4gLQwPdKqa1KqeluElsrIBWYb29Om6OUCnSDuAoaB3xi/9qlcWmtjwGvAkeBE8A5rfX3ro4LiAOuVkqFKqUCgD8BzR0ZlycnAreglKoDLAMe0lqfd3U8AFprqzZu25sBve23pi6llBoFnNJab3V1LKW4SmvdE7geo5nvalcHhPGutifwnta6B5CBa5vOClFK+QCjgc9dHQuAvY19DNASaAIEKqUmuDYq0FrvBf4N/ACsBnZiNC07jCcngpNKqcYA9s+nqjsApZQ3RhL4WGv9hbvEdYnW+izwE0b/iqvjugoYrZRKAD4FrlVKLXKDuADQWh+3fz6F0d7d2w1iSwaS7Xd0AEsxEoOr47rkemCb1vqk/bGr4xoKHNFap2qt84AvgP5uEBda67la655a66uBNOCgI+Py5ESwArjT/vWdGG301UYppYC5wF6t9etuFFcDpVQ9+9f+GP8c+1wdl9b6Ca11M611JEZzwlqt9QRXxwWglApUSgVd+hqjXTnO1bFprVOAJKVUe/umIcAeV8dVwHguNwuB6+M6CvRVSgXY/z+HYHSuuzoulFLh9s8tgJsxfm6Oi6s6Oz1c9WH/oZ0A8jDeJU0FQjE6Hg/aP9ev5pgGYLQr7wJ22D/+5AZxdQO22+OKA562b3dpXEViHMzlzmKXx4XRFr/T/hEPPOVGsUUBsfbf53IgxE3iCgDOAHULbHOHuJ7DeOMTBywEfN0krl8xkvhOYIijf15SYkIIITycJzcNCSGEQBKBEEJ4PEkEQgjh4SQRCCGEh5NEIIQQHk4SgRBlUEpZ7RUy45RSn9un+Jd03G/VHZsQjiKJQIiyZWmjUmYXIBeYUXCnUsoMoLXu74rghHAESQRCVNyvQBul1GBlrCWxGNgNoJS6eOkgpdRj9rUJdiqlXrZva62UWm0vSverUqqDa74FIYrzcnUAQtQESikvjNo4q+2begNdtNZHihx3PXAj0EdrnamUqm/fNRuYobU+qJTqA7yLUU5bCJeTRCBE2fztJbnBuCOYi1GIbHPRJGA3FJivtc4E0Fqn2SvM9gc+N0rYAEbpAiHcgiQCIcqWpY2S3PnsL+YZpRyvMGpIFWQCzhY9jxDuQvoIhHCs74Epl0YXKaXqa2OdiSNKqVvt25RSqrsrgxSiIEkEQjiQ1no1RnngWHuT0t/su+4ApiqlLlUoHeOaCIUoTqqPCiGEh5M7AiGE8HCSCIQQwsNJIhBCCA8niUAIITycJAIhhPBwkgiEEMLDSSIQQggP9/+nMG+pU6o1GAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve = (df_cop[[f\"claim_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                 .melt()\n",
    "                 .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                 .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "ortho_df = orthogonalize(orthogonalize(df_cop, x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"claim\")\n",
    "\n",
    "factual_claim_curve = (ortho_df\n",
    "                      .groupby(\"price_deb\")\n",
    "                      .apply(lambda d: np.average(d[\"claim_deb\"])))\n",
    "\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim|price)\")\n",
    "\n",
    "sns.scatterplot(data=(ortho_df\n",
    "                      .groupby([\"price_deb\", \"model\"])[\"claim_deb\"]\n",
    "                      .mean().reset_index()),\n",
    "                x=\"price_deb\", y=\"claim_deb\", hue=\"model\")\n",
    "\n",
    "plt.ylabel(\"Claims\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26a7c9d",
   "metadata": {},
   "source": [
    "## Effect on Converted \n",
    "\n",
    "### IPW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b533fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:25.470078Z",
     "start_time": "2023-07-13T18:15:24.992670Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve_conv = (df_cop\n",
    "                      .query(\"conv==1\")\n",
    "                      [[f\"claim_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                      .melt()\n",
    "                      .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                      .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "factual_claim_curve = estimate_factual_curve(estimate_ps(df_cop.query(\"conv==1\"), x=[\"model\"], t=\"price\"),\n",
    "                                            \"claim\", x=[\"model\"], weight=\"w_est\")\n",
    "\n",
    "plt.plot(cf_claim_curve_conv, label=\"P(claim(price) | conv=1)\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim | price, conv=1)\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Claim\")\n",
    "plt.xlabel(\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "88889095",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:25.540581Z",
     "start_time": "2023-07-13T18:15:25.471234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factual_conv_curve = (df_cop\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"conv\"], weights=1/d[\"ps\"])))\n",
    "\n",
    "plt.plot(pd.DataFrame(factual_conv_curve.values*factual_claim_curve.values, index=factual_claim_curve.index),\n",
    "         label=\"P(conv|price)*P(claim|price)\")\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec834e2a",
   "metadata": {},
   "source": [
    "#### Not Estimating the Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebf78ec1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:25.701194Z",
     "start_time": "2023-07-13T18:15:25.541958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plt = (df_cop\n",
    " .query(\"conv==1\")\n",
    " .assign(size=1)\n",
    " .groupby([\"model\", \"price\"])\n",
    " .agg({\"size\": \"sum\", \"ps\":\"mean\"})\n",
    " .reset_index()\n",
    " .assign(est_ps = lambda d: d[\"size\"]/d.groupby(\"model\")[\"size\"].transform(\"sum\"))\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(data=df_plt.assign(model = lambda d: d[\"model\"].astype(str)+\"_ps\"),\n",
    "             y=\"ps\", x=\"price\", hue=\"model\", palette=\"Blues\")\n",
    "\n",
    "sns.lineplot(data=df_plt.assign(model = lambda d: d[\"model\"].astype(str)+\"_est_ps\"),\n",
    "             y=\"est_ps\", x=\"price\", hue=\"model\", palette=\"Reds\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0fe283ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:26.209333Z",
     "start_time": "2023-07-13T18:15:25.702540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve_conv = (df_cop\n",
    "                      .query(\"conv==1\")\n",
    "                      [[f\"claim_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                      .melt()\n",
    "                      .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                      .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "factual_claim_curve = (df_cop\n",
    "                       .query(\"conv==1\")\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"claim\"],  weights=1/d[\"ps\"])))\n",
    "\n",
    "plt.plot(cf_claim_curve_conv, label=\"P(claim(price) | conv=1)\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim | price, conv=1)\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Claim\")\n",
    "plt.xlabel(\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97c8ce62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:26.279692Z",
     "start_time": "2023-07-13T18:15:26.210425Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factual_conv_curve = (df_cop\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"conv\"], weights=1/d[\"ps\"])))\n",
    "\n",
    "plt.plot(pd.DataFrame(factual_conv_curve.values*factual_claim_curve.values, index=factual_claim_curve.index),\n",
    "         label=\"P(conv|price)*P(claim|price)\")\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f3ddf9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T15:54:33.421573Z",
     "start_time": "2023-07-13T15:54:33.413638Z"
    }
   },
   "source": [
    "### Orthogonalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71dab5ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:26.820266Z",
     "start_time": "2023-07-13T18:15:26.281037Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve_conv = (df_cop\n",
    "                      .query(\"conv==1\")\n",
    "                      [[f\"claim_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                      .melt()\n",
    "                      .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                      .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "\n",
    "ortho_df = orthogonalize(orthogonalize(df_cop.query(\"conv==1\"), x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"claim\")\n",
    "\n",
    "factual_claim_curve = (ortho_df\n",
    "                       .groupby(\"price_deb\")\n",
    "                       .apply(lambda d: np.average(d[\"claim_deb\"])))\n",
    "\n",
    "plt.plot(cf_claim_curve_conv, label=\"P(claim(price) | conv=1)\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim | price, conv=1)\")\n",
    "plt.legend()\n",
    "\n",
    "sns.scatterplot(data=(ortho_df\n",
    "                      .groupby([\"price_deb\", \"model\"])[\"claim_deb\"]\n",
    "                      .mean().reset_index()),\n",
    "                x=\"price_deb\", y=\"claim_deb\", hue=\"model\")\n",
    "\n",
    "plt.ylabel(\"Claim\")\n",
    "plt.xlabel(\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cec7c4d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:26.976035Z",
     "start_time": "2023-07-13T18:15:26.821711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factual_conv_curve = (orthogonalize(orthogonalize(df_cop, x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"conv\")\n",
    "                      .groupby(\"price_deb\")\n",
    "                      .apply(lambda d: np.average(d[\"conv_deb\"])))\n",
    "\n",
    "\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "plt.plot(pd.DataFrame(factual_conv_curve.values*factual_claim_curve.values, index=factual_claim_curve.index),\n",
    "         label=\"P(conv|price)*P(claim|price)\")\n",
    "\n",
    "\n",
    "ortho_df = orthogonalize(orthogonalize(df_cop, x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"claim\")\n",
    "sns.scatterplot(data=(ortho_df\n",
    "                      .groupby([\"price_deb\", \"model\"])[\"claim_deb\"]\n",
    "                      .mean().reset_index()),\n",
    "                x=\"price_deb\", y=\"claim_deb\", hue=\"model\")\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0018cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T16:04:34.356762Z",
     "start_time": "2023-07-13T16:04:34.341019Z"
    }
   },
   "source": [
    "#### Not Estimating the E[T|X, conv=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "152e76f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:27.567497Z",
     "start_time": "2023-07-13T18:15:26.977312Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n5syZLFmyhOzsbM6dO8eMGTMYPnw4MTEx2Gw2nn76aX755ReUUkyYMIGHH36YrVu38thjj5GRkUF4eDgzZ84scYZvRkYGDz/8MFu2bEEpxZQpU7j++uuZM2cOr7/+Olprhg0bxn//+18AgoKCmDRpEosXL8bf358ff/wRPz8/oqKiiI2NxWQykZmZSatWrYiNjcXb27vE9x4YGEjv3r0vmH193ogRI+jTpw/PP/98mX+W4hLUag6XDzNW5ur9qFvcOHWqhK1wbCsMfQtM7vM53H0iqSbOl6Fu3759iWWod+zYwVNPPXXR6x966CE2b95MTEwMWVlZLF68uMjjnC9D3adPn/yupT/++CO/hHV5HD58mDVr1rBkyRImTpyYX/No48aNfPnll6xcufKC/adPn05cXBzbtm1j586djBs3DovFwsMPP8z8+fPZunUrd911V6kn0VdeeYXg4GD+/vtvdu7cycCBAzl+/DhPP/00K1euZPv27WzevJmFCxcCcO7cOXr06MGOHTvo27cvn376KcHBwURFRbFmzRoAfvrpJ6666iq8vb1566236Nix40VfjzzySKk/k9DQUHJycip1Qp7H6jXZWJBl29eujsT5Nn0CPjWgY4lV+itd9bsiKMcnd0dytzLU5Vlh7MYbb8RkMtGiRQuaNm3K3r17AaNsdVExLl++nIkTJ+Ll5ZX/PmJiYoiJiWHw4MEA2Gy2Uuv9LF++nLlz5+Y/Dg0NZe3atfTv3z+/hPa4ceNYu3YtI0eOxMfHJ3/pzS5duvDbb78BcNNNN/Htt98yYMAA5s6dm78mwZNPPsmTTz5Z5p9DYedLUJ8v3S0cqOBE1oZdodEVsPEDo/CaueQruSorIxFiFkD0XeBbw9XRXKD6JQIXcbcy1OXhiPLUWmvatm3Lxo0by3zc4topjre3d/7+BctPjxgxgmeffZaUlBS2bt3KwIFG3+tbb72VX8G0oL59+zJt2rRS45MS1JUh7/+/1ySYM9a4kdrBNTX5nW7rTLBboJv73CQ+T7qGnMiVZajL47vvvsNut3Po0CFiY2Np1apVifsPGTKEjz/+OP9EnJKSQqtWrUhKSspPBBaLhV27dgHG4vAFy1YXbKfg9tTUVLp3786aNWs4ffo0NpuNOXPm0K9fvxLjCQoKolu3bkyaNInhw4fn35h/8skniyw/XZYkoLXm5MmT+d16wslaXAXhrWD9exdeLVQXNgtsmQHNBkF4c1dHcxFJBE7mqjLU5dGqVSv69evH0KFD+fjjj/HzK3lyzz333EOjRo3o0KEDUVFRzJ49Gx8fH+bPn8/TTz9NVFQUHTt2ZMOGDYCxtGRR3SsvvPACqamptGvXjqioKFatWkW9evV44403GDBgAFFRUXTu3Jnrrruu1Pdw0003MWvWrPwy1mUVGRnJY489xsyZM4mIiMhfeW3r1q306NEjv/tLOJnJBD0fhlN/w6GVpe9f1exZBGdPQPfKX5i+TIorS+quX1KG+tL069dPx8XFXbT9jjvu0N99951Tjz1s2DCdk5Pj1GM42iOPPKKXL19e4j7u/Hvn9rLOGOWX1//vn22WbK3faqn1lyNcF5ezfDZE63ejtLbZXBYCJZShlisCJytYhtpTLV68GJ8qVlmyXbt2DBo0yNVhVH8F7xF5+UKP+yF2NRzf7qqIHO/4doj/A7pNcKshowW5Z1TVzF133VXpaxEUNn78+CJHEs2cObPazRh2hAkTJrg6BM8UfacxvHJD6fdxqoxN08E7EDqOc3UkxZJE4CGKSwRCuBW/YIgeb4weSj3s4mAc4Nxp+Hs+RI0F/xBXR1MsSQRCiMpX0sig7veDMsHGalDi468vwZbjlkNGC5JEIIRwoSLm1QQ3gPY3GDONM1Mufr6qsFlh8wxo0g/qXO7qaEokiUAI4X56PgyWTKMGUVW1bwmkJ7jvkNECJBE4yF133UWdOnVo166dq0MRouq7rI0xyezPj8GS5epoLs2f0yGkEbS82tWRlEoSgYOMHz+eZcuWuToMIaqIvHsEJZRcodcjkJkM2y8uE+L2TsbAkXVG7SSTa0cMloVHJoKc1GTO7NlJys4tnNmzk5zUileY7Nu3b5EF2oQQl6hxL2jQBTa8byzrWJVsmg5e/tDpNldHUiYelwhyUpM5l3AEuyUXALsll3MJRxySDIQQDqSUUYwuNQ72/OTqaMrElmslN+kYeuc8o3heQNX4cOhxiSDr5DHQ9gs3aruxXQjhXi4fbqzrWwWK0WUcT+LADytInPECyppF9uXuO4GsMI9LBOevBMq6XQjhBPkn9RLuEYDRv37FQ3D8Lziy3ulhXaqslDT2fvsLabEJhGevJl014/C2dGy5FleHViYelwhM3kXXvCluuxDCxTreAgHhxlWBm8pOTsOWY6GubQ1+OoWTXn1JiztGTlqGq0MrE6clAqXUDKVUolIqppjnlVJqmlLqoFJqp1Kqs7NiKci/bgNj1uIFwZiM7RVw8803c8UVV7Bv3z4iIiL4/PPPK9SeECKPt78xFv/Ar3Bqt6ujKZLJy0yEZQmNrT+SampLqqkdysuMycv9RwyBc1comwm8D3xVzPNDgRZ5X92Bj/K+O5VvqFEXP+vkMeyWXEzePvjXbZC//VLNmTPHEeEJIYrS9R5Y93+w4X8w6iNXR3Mhm4WgLVMItv1GovkK4rzGgDIT0acTviHutSRlcZyWCLTWa5VSkSXsch3wVV6d7D+UUiFKqXpa6xPOiuk839BaFT7xCyEcoKR5BAUFhBlDMbd8DgNfMMpQuIOcszDvdsyHVmLt+STmhuNomJ5BYJ0wAuvVLnFpWnfiyuWXGgDxBR4n5G27KBEope4F7gVo1KhRpQQnhKezZueSeToVW1YOvqE18K8V4toT2xUPGiUn/vwIhrzqujjOO3sSvrkBTu2CEe/j1fk2qurHS1cmgqJ+o4ocH6a1ng5MB4iOjnbvMWRCVAOWrBziV28hacd+AJSXmVZjriQ4sr7rggptDG1HwZaZ0OcJ15Z1TtoPs66HzNNwy7fQYrDrYnEAV44aSgAaFngcARx3USxCiAIyTyWTtGM/Zp1JkD0ObbUR9/N6LJkurvvT6xHIPQtbv3BdDEf/hBlDwJoF45dU+SQArk0Ei4Db80YP9QDSKuP+gBCidJZzWQTYj9Eu923a5r5HsG0POWkZWLMdNC6+rPMICqsXBU37wx8fgzXHMbGUx57F8NUI8A+Du3+DBpUy2NHpnDl8dA6wEWillEpQSt2tlJqolJqYt8tSIBY4CHwKPOCsWIQQ5ROU/Dttct/DpK1kqdo0tcylZv0gfAL9XR2aUXYi4yTsnFe5x930Kcy7DS5rB3f/CmFNKvf4TuTMUUM3l/K8Bh501vErW3x8PLfffjsnT57EZDJx7733MmnSJFeHJUT52G2w4mX81r9HblgHduWOxTv3NG1z36WFzy+YfW9wdYTQdADUbW+sa9xxnPMXhNcaVrxsDF9tORTGzACfAOces5K58mZxteLl5cXbb79N586dOXv2LF26dGHw4MG0adPG1aEJUTaZKfD93XBoJUTfhc/V/6X12WxsuRb0dhteG9+D/TdAy6tcG6dS0HMSLLgHDvwCrYY671jWXFj0MOycC13uhGumgrn6nTar3zsqg9O7DhG/Ziu56efwqRlIw35dCG/brEJt1qtXj3r16gFQo0YNWrduzbFjxyQRiKrh1C6YewukHYNr34Mu4wHwC80rvTLoeTj0Gyx6BB78A/xDK3jAMqxHUJK2I2HFv42yE85KBNnpRldQ7Gpj7kKfJy49XjfncbWGTu86RNzP68lNPwdAbvo54n5ez+ldhxx2jMOHD7Nt2za6d3f6RGkhKm7XQvhsMFiy4c6l+UngAl6+MPJDOJcEPz9T2RFezOwNVzwARzdC/CbHt59+AmZeA3G/w3UfQt8nq20SAA9MBPFrtmK3XrjIhd1qI37NVoe0n5GRwfXXX8+7775LzZo1HdKmEE5ht8Hyl+G7O+CytnDvamjYrfj963eCPo8b3SR7l1RamMXqdBv4hTi+GF3SPvh8MCTHwrh50KnqlJO+VB6XCM5fCZR1e3lYLBauv/56xo0bx+jRoyvcnhBOk5UKs2+Ede9A5ztg/GKoWa/01/V90hg189Nk457CpXLE2gK+QUYNor1L4PTBircHcPQP+HyIMTT1ziXQ/ErHtOvmPC4R+NQMLNf2stJac/fdd9O6dWsee+yxCrUlhFMl7oHpAyB2DQz/Pxgxzej6KQsvHxj5EWSlwM9POTfOsuh+H5h9YOP/Kt7Wnp/gq+sgMBzu+c24AvIQHpcIGvbrclFpWJOXmYb9ulSo3fXr1/P111+zcuVKOnbsSMeOHVm6dGmF2hTC4XYvgk8HgSXTuAqIvqv8bdTrYFwZ/P2d0Z4rBdUx1ivYPgfOnrr0dv6cDt/eBnU7wF2/Qmikw0KsCjxu1ND50UGOHjXUu3dvtJsvpSc8mN0Oq16D36dCg2i46WuoWYG6QX0eh72LYfGj0Lin8SnaVXo+DFtnwqZPYNCL5Xut3Q4rXsobfTQMrv+s2s0RKAuPSwRgJIOKnviFqDKyzsCCCcbCLp1ug2Fvl70rqDhmbxj5MUzvD0ufgBtmlrOBCg4fLahWM2g93KhM2vsx495BWVhz4ccH4e95EH03XPOWsTSmB/K4riEhPEriXvh0oDFJbNjbMOJ/FU8C59VtB/2fhl0/GF+u1GsyZKfBX8Wtg1VIdjp8M8ZIAgP/ZfxsPDQJgCQCIaqvPYvhs0GQkw53/GSMsHH0WPhej0K9jrDkcchIcmzb5RERDY16wsYPwFZKYbz0E/DFUDiy3riq6Vt9J4qVlSQCIaobux1WvQ7fjoPwlnDvGqMfHxx/H8vsBaM+NlbqWvKYY4aFXqpekyA9oeSrk8S9xhyB1MNwyzzoWGJJNI/hkfcIhKi2stNgwX2w/2ejINuwd8Dbj5hjabzx8x7+jE0hyM+LYH9vavp5G9/9/3lc09/4Mh57Ffi38d3Hq4jPjnVaQ/9njcJsMd9D+zGlx3mpZahL0mII1L7cuPHb/oaLP+Uf2QBzxoKXnzGDul6U445dxUkiEKK6SNpv1AtKjTOKo3W9h2Np2bz9y3YWbDtGaIA3d/SMJNdqJz3bQlqWhfQsCyfSskjPtpKWZSHXai/xEH7epkJJxPge4juQe4PmE/rjY/ya1gyfkHoXJJjgAG+CfLwwmZzYBWMyGSOIfnwQDq24cDLY7h/h+wkQ0ghu/d5Y7Uzkk0TgINnZ2fTt25ecnBysVitjxozh5ZdfdnVYwlPsXQoL7jVuBN++iPS63fjol318vi4OgPv7N+P+/s2o6eddYjPZFhvp2UaCSMuykp5luSBpGN+NpJGebeFUejYHEs+SnmXl95zxLPF+Dr9fHudey2MU/rRvUlDDz7gCaeybwSxg9qaj7Diyk+AA4wrkfHI5f3US7O+V/9jPuww3c9vfgF7xCpYVb3PK0oLg2qHUPDgblj1jlM+4eS4EhF3iD7n6kkTgIL6+vqxcuZKgoCAsFgu9e/dm6NCh9OjRw9WhierMboe1b8LqN6BeRyw3fM2cfXbe/Xo1KedyGdWpAY8PaUlEaNnGxvt5m/HzNlOnht8lhKLJ/f0cQ1a9xLorT3M0YjjpxSQTMiyQCklnc1idlkhaloVsS8lXI75epgu6rYIv6MYyvvubobH5Kvqc+JJ5r7/DFbUP0LPmenSrYagxn4O3Gyys44Y8MhHErN7Bqq+Wk3Y6jeDwYAbcfiXt+lesv1ApRVCQMX7ZYrFgsVhQHj4SQThZdjr8MBH2LUFHjWVFs2d5fUYssafP0aNpGM9f04b2EcGVFo7JpPDr8wgcWErExilEPHBV8fWLzp6Et2HSlS2ZFG104eRYbUbiKHwFkp2XTC5IKFZOZ+QSe/pc/r72vNsONejDBt953NN0HsEqk6+sg3l5x80E7Ftb4r2RwtvOJ5o6NXyr/d+yxyWCmNU7WPL+Iiw5xhCztKQ0lrxvTJOvaDKw2Wx06dKFgwcP8uCDD0oZauE8pw/C3Jsh+RAJPV7isbjubPpzF81qB/L5HdEMvLyOa05eJrNRi+jjXrB4stEVU8Y4fL3M1K5hpnaN8s9z0FqTkWNl8+q/+emzX9lYtxNDwjfwbfJglqYP4K4RDbF4e1+QTA6fzszv4srMtRXb9sHXhuJllkRQraz6anl+EjjPkmNh1VfLK5wIzGYz27dv58yZM4waNYqYmBjatWtXoTaFsOTkknwsmZzMHELrhlEzeQMsmIBNefFBxFTeWX0Z4UHneHVkO8Z2bYiX2cWjwsObG6UefnkOdswxagE5mVKKGn7eNG8YRq2cTDYf6cLBk81JyQmlfyPN7YNbElCz+O6xXKuds9kXXoGkZVk4l2N1/c+zEnhcIkg7nVau7ZciJCSE/v37s2zZMkkEokIyz2ay4bvf+WPhBtB2BjXbSY+QNZwKaMHYtIc5ea42Dw9syn39mhHk60Z/zt0nGtU8f34GmvavWF2jcqgTWZfeY/ux7tu1pOSE4hfkz7AHry0xCQD4eJmoFeRLrSAHzbquYtzoN6dyBIcHk5Z08Uk/OLxifalJSUl4e3sTEhJCVlYWy5cv5+mnn65Qm0KcPHicP35Yj48pl2ub/krrkEMssvXiqZQJDO/SlMeHtKResBveADWZ4boP4KNexvKW4767sIvIGfMIAL9AP3qN6UurHq3Jzsgi5LJQQuvKKKHSeFwiGHD7lRfcIwDw9vVmwO0VW4DixIkT3HHHHdhsNux2OzfeeCPDhw+vaLjCw6UlnkGjubrlcloExPFvy238kt6TryZ2o1vbBq4Or2S1msHgl411C7bNgs63VcphvX29qdescq5AqguPSwTn7wM4etRQhw4d2LZtmyNCFCJfgsmX2q0P0yHgAK9l3sqx+Cbc38xCx2a1XR1a2XSdYKxZ8MtzRhdRSENXRySK4HGJAIxkUNETvxDOdCT5HG8u28eZXb/ytf9PrDsbhW1/GG3DNVfeOQQfPx9Xh1g2JhNc935eF9HDcNsPHl/gzR15ZCIQwl2dyczlfysP8tXGwzQwpbI04CN0cEta3PIFDS1mY9RQeE1Xh1k+YU2MLqKlTxgLyETfiUPXIxAVJolACDeQY7Xx1YYj/G/lATJyrNzcuS4vpbyNd7IVbv6Gy8Kr+EJK0XfDnkXw6wvQfBCY5NTjTsr0v6GUCgFuByILvkZr/YhTohLCQ2it+WnnCd5ctpeE1Cz6t6rNs0Nb02r767Bri7HyV3gLV4dZcSYTjHgfPuppFIUb+ZGrIxIFlDUtLwX+AP4GSi4IIoQok01xKby2dA874s/Qul5Nvr67PX1a1Dbq6f/xIXS/H9qOcnWYjhPaGIa8AosfJWfDdHyBs2czqeHquESZE4Gf1voxp0YihIeITcrgv8v28suuU9St6cfUG6IY1akBZpOC0wfgx4cgohsM/rerQ3W8LndybvNc/P6YBgqmT/uKrvZO9OrXDbPZc5eKdLWyzp3+Wik1QSlVTykVdv7LqZFVUTabjU6dOskcAg9U2upfyRk5TPkxhiH/t5Z1B07zxJCWrHqiP2O6RBhJIPccfHubUUr6hpngVUVGBpVDeloGT/1WmyyLcZP4bHoGkyc8T+yBIy6OzLOV9YogF3gLeJ782/1ooKkzgqrK3nvvPVq3bk16erqrQxGV5Fj8CVb+uo4VP6+le6/OXD1iEE2aNcp/Ptti44v1h/lw1UEyLTZu7taQSYNaXlhcTWtY/Bgk7YXbFkCwm08Wu0SnTibx+9YE3kpvwMu947HYFVarjaNxCbS4XE4nrlLWRPAY0FxrfdqZwVSWJQt/Y9qbn3LyeCJ169fhkacmMGzk4Aq3m5CQwJIlS3j++ed55513HBCpcHfnzmUy9dUPWbFsLQB/bd7J8mVr+eTrqYSFh7Fox3He+mUfx85kcWXrOjwz9HKa1ymiV3zrF7BzLvR/DpoNrOR3UXkCawQSHFKTHw5oUrK92HzCKN0eElZ55bLFxcraNbQLyCxv40qpq5VS+5RSB5VSzxTxfLBS6iel1A6l1C6l1J3lPUZ5LVn4Gy8/8xYnjp1Ca82JY6d4+Zm3WLLwtwq3PXnyZN58801MpupfrVAY4g8fY8WytTQIymFq/zjqBeZyYG8si/7Yz4gP1jH52+2EBfowZ0IPPruja9FJ4Pg2+PlpY2nFvk9W/puoRPUbXMbzrz6KUibWxAeTaTVz/djhcjXgYmW9IrAB25VSq4Cc8xtLGj6qlDIDHwCDgQRgs1JqkdZ6d4HdHgR2a62vVUrVBvYppb7RWueW942U1bQ3PyU7K+eCbdlZOUx789MKXRUsXryYOnXq0KVLF1avXl3BKEVVoZSiQVAOM4YepF6QhcUnG3Go9mj+vSGFBiH+vHtTR0ZE1S9+rd7MFJh3OwRdBqM/NYZZVnMDr+rN7J8+If7wMcJqhdKqTTNqBsvYIVcqayJYmPdVHt2Ag1rrWACl1FzgOqBgItBADWWsoBEEpADWch6nXE4eTyzX9rJav349ixYtYunSpWRnZ5Oens6tt97KrFmzKtSucG+NQ+3MHn2MIGX82uZ0Hoi2NubhPo14cEibktfZtduNFcbST8Bdv3jMWro+Pj60bd+Ktu1buToUkadMiUBr/eUltN0AiC/wOAEovGTX+8Ai4DhQA7hJa33RPAWl1L3AvQCNGjUq/HS51K1fhxPHThW5vSLeeOMN3njjDQBWr17N1KlTJQlUd3YrPt/ehPax80LOvfzX9AktA208eH1HOrSOLP31696BA7/ANVMhoovTwxWiOCVehyql5uV9/1sptbPwVyltF3UtXHh83VXAdqA+0BF4Xyl1USEVrfV0rXW01jq6du2KVV185KkJ+PlfuPiEn78vjzw1oULtCs+irbnYzyaSeSaJmzKfJiCyKwCPj+patiQQuwZWvQbtxkDXe5wbrBClKO2KYFLe90sZFJ8AFKw5G4Hxyb+gO4H/aGMA9kGlVBxwObDpEo5XJufvAzhj1NB5/fv3p3///g5rT7iXc5lZeJ85BNrGyyGv8q+Ro+jqfxI+gos/6xQh/Th8fzfUagHXvieF14TLlZgItNYn8r5fymyPzUALpVQT4BgwFii8eOlRYBDwu1LqMqAVEHsJxyqXYSMHO/TELzxDtsVG4pkM6uTGY1ZWcv3CeXPSYGOR+MS87sZSJpVhs8B3d0JuJoxfAr5Bzg9ciFKUaYiCUqqHUmqzUipDKZWrlLIppUqcMaW1tgIPAb8Ae4B5WutdSqmJSqmJebu9AvRUSv0NrACeri5zFUT1YbHZSUjNJO7UGS6zxOOjbKhazfHz8zeSAPBPT2gpiWD5SxD/B4yYBrXlZqlwD2UdNfQ+xif674BojEqkzUt7kdZ6KUbBuoLbPi7w7+PAkLIGW8qxCvxRClFxNrvmdEYOSWdzMGOlufkkXthQYc3QPoEX7nz+d6+kK4Ldi2Dj+9DtXmg/xnmBC1FOZS4KrrU+qJQya61twBdKqQ1OjKtc/Pz8SE5OplatWpIMRIVprUnNzOVkeg5Wm50wPxP1bYmYbFao1RTtE0hycjJ+fn4FXlXK713yIaP8coMuMORVp8YvRHmVNRFkKqV8MCaVvQmcAAJLeU2liYiIICEhgaSkJFeHIqq4bIuNtCwLFpvG18tEsJ+Js9nJ7LNbILA2pCUAxoePiIiIf15Y0geQ3EyjmJzJC2740igqJ4QbKWsiuA0wY/T5P4oxGuh6ZwVVXt7e3jRp0sTVYYgqbNfxNN5Yupd1B0/TuFYAz1x9OVc29UF9dR2c3g+3zIVmnUpooZiuIa1hyeOQuBtunS+Ltwu3VNYJZedHDWUBLzsvHCEq14m0LN7+dT/f/5VAsL83U65tw7jujfGxpMGXI4wkcPPschSCK5QI/voKdsyGfs8YtYSEcEMlJoK80TzF3v3SWndweERCVIKMHCufrDnEp7/HYrfDvX2a8sCA5gT7e0PWGfhqpFESeuzssp3Ai7pZfHw7LH3SSCL9nnLCuxDCMUq7IhgNXMaFpSIAGnPx5DAh3J7VZmfu5njeXb6f0xm5XNexPk8MaUXDsABjh+w0+HoUnNoFN82CFuWdb5KXCLJSjWJygeEw+jMwyepbwn2Vlgj+D3iu8ISyvEqh/wdc66zAhHAkrTUr9ybyxs97OZiYQbcmYXx+R2uiGob8s1N2Osy6Hk7uhBu/hlZXl/0ABa8I7HZY+ACkH4M7l0FgLYe+FyEcrbREEKm1vqimkNZ6i1Iq0jkhCeFYfyek8drS3fwRm0LT2oFMv60Lg9tcduFQ45yz8M0YY22AG2bC5deU8ygFJpRteA/2LYWr/wsNuzroXQjhPKUlAr8SnvN3ZCBCONqxM1lM/WUfP2w7RligD69c15ax3RrhbS40oT4nA765ARK2wA1fQOvyXejabVZsmefwBmz7V2DaswDVdhR0v89xb0YIJyotEWxWSk3QWn9acKNS6m5gq/PCEuLSpWdb+HDVIWasj0MBD/RvxsT+zajp533xzrnnYPaNEL8Jrv8M2lxXrmNprclNTSY7/jAhgHn3fGw1G6OueQeTTG4UVURpiWAy8INSahz/nPijAR9glBPjEqLcLDY7s/88ynsrDpByLpfRnRvwxJBW1A8p5uI1NxNm3wRHNxqrg7UbXe5j2nNzyDxxLL9olzb7kdHtRQK0V5nXgRXC1UqrPnoKoyjcAKBd3uYlWuuVTo9MiDLSWvPr7lP85+e9xJ0+R89mtXjumta0axB80X7ZqenYLVZ8A8x4LbgdDq+D0dMvufaPtttB27H7BmOr0Yis1rdjqxmJttkc8daEqBRlnVC2Cljl5FiEKLft8Wd4bcluNh9OpUWdIL4Y35X+rWpfVHPKmpNL0s4DxK/ZCpYs2pi+IjBrF2rkR9Dhxks+vsnHF6/AGljPQdrgL/I2mjD5lnR7TQj3Uuaic0K4k2yLjZd/2sWcTfGEB/ny+qj23BgdgVfhG8F5Mk8lc3TFJpS20NIyg0D7PhIbPkTtdjdUqAvHZDYT2KARWYknyU1PxeznT0C9CLz8ZCyFqDokEYgqJz4lk/u/2UrMsXTu69uUhwe1IMi3hF9luw3bvhVEWr4lzLYDbzKJ9RrL6dSWhGTm4FuzYn8GZj9/AiMa429tgDKbMZll8pioWiQRiCpl1b5EJs/djl1rPr8jmkGtLyt6R7sdEjZDzPeweyGhGaew4UOqqR1J5h6km1sSVCcMs28RI4kugTKZMPv4OKQtISqbJAJRJdjtmmkrD/DeigNcXrcmH9/amca1ClVC1xpObDdO/rsWQlo8mH2h5RAsza/lyJFgkvefAMDs402jQd3w8pWTtxCSCITbO5OZy6PfbmfVviRGd27AayPb4+9ToPslcY9x8o/5HlJijbr/zQbBwBeg1TXgVxNvoHGbHOpEp2LLteAXVhP/sOBijymEJ5FEINxazLE07v9mKyfTsnl1ZDvGdW9kjAhKPgS7FkDMAqPWvzJBZB/oNdmYGRwQdlFb3v6+eDeqW/lvQgg3J4lAuK15W+L518IYwgJ9mHffFXSqmQEb/md88j+x3dip0RVwzVRjRnBQHZfGK0RVJYlAuJ0cq42XFu1mzqajDI1UTG2zl8Df3oT4P4wd6ncy1v1tOwqCI0puTAhRKkkEwiVsVit2m8a70KidY2eyePqrVUScWsHqOttpfOov1Ek71GkLA/9llIEIa+qiqIWoniQRiEql7Xbidx9l4w/ryUjNoOvwbjSPbkWAj5V9a+aSuHE2X+ideHvbwKsZ9HnCOPnXae3q0IWotiQRiEp14uBxZr0wE7vNjpeycGDmKhr+fRafM5topXMJUbU51+k+QrqNhbod/lnwRQjhNJIIRKWK33MUu81OVK1dXNVwDT5mC6eTg/nKPpC0Ztdy3y03EeCgSV5CiLKRRCAqlZePF/7mLAY3XMuxnDq8Yh/HWnN7nr66NZP7N7+oWJwQwvkkEYhK1bBNY/o12oqPycJE9TDxui5v9m3I6AEtXB2aEB5LEoGoVCEB5wgL28E8az+0fyO+vro5HTrJKCAhXEkSgag0J9KyOPDZZLpqE4nRj7H02j7Flo0WQlQeSQTiIrk5ucQePMKpk0lcVrc2zVpE4u1TsRu4Gw6d5oNv5vONfQ0HL7+PR0b2c1C0QoiKkkQgLmCxWFn0/S+88tzbaK0xmUxM+c+TjBhzFeZLqLOvteaTtbG8uWwP8wNmY/MLpfmo550QuRDiUsl1ubjAkbh4Xn/xXbS207FOBna7jVdfeIcjcQnlbutstoX7Z/3Ff37ey2NNEuhs24G5/9PgJ1U/hXAnTk0ESqmrlVL7lFIHlVLPFLNPf6XUdqXULqXUGmfGI0qXcvoMVouVUS1S+GrYQdrUysKSayHldGq52tl/6izXvb+e3/ac4l/XtORB29cQ0hii73JS5EKIS+W0riGllBn4ABgMJACblVKLtNa7C+wTAnwIXK21PqqUkvKRLla3fh1q1vDn7g6nAPD3slOjZhB165f9v2bRjuM8PX8ngb5ezL6nO93Tf4VTMXD95+Dl66zQhRCXyJlXBN2Ag1rrWK11LjAXuK7QPrcAC7TWRwG01olOjEeUQaPIBsx4sT+NauYCUDMkiKkfvkxEo/qlvtZis/PyT7t4ZM422tavyZJHetO9YSCseg3qdYS2o50cvRDiUjjzZnEDIL7A4wSge6F9WgLeSqnVQA3gPa31V06MSZTGbqdl0g9okzfKbuHF15+gVnR0qS9LTM/mgW/+YsuRVO7sFclz17TG22yC9dOMJSOv+wBMcktKCHfkzERQVK0AXcTxuwCDAH9go1LqD631/gsaUupe4F6ARo0aOSFUkW/vYkjai+oyHrbOpFatkFJf8mdsMg/O3sa5HCvTbu7EiKi8q4esVPj9bWh+JTSV4aJCuCtnfkRLABoWeBwBHC9in2Va63Na69PAWiCqcENa6+la62itdXTt2rWdFrDH0xp+n2rU+287qgy7az77PZZbPvuTmn5e/PhQr3+SAMDv70B2Glz5shODFkJUlDMTwWaghVKqiVLKBxgLLCq0z49AH6WUl1IqAKPraI8TYxIlObgcTuyA3o8ZC8ADF1/EGTJyrDw0exuvLtnDla3r8ONDvWh5WY1/djgTD39+AlFjoW4758cuhLhkTusa0lpblVIPAb8AZmCG1nqXUmpi3vMfa633KKWWATsBO/CZ1jrGWTGJEmgNa9+C4IbQ4SZI2FTsrgcTM5g4ayuxSRk8O/Ry7u3b9OKqoateN74PkMljQrg7p84s1lovBZYW2vZxocdvAW85Mw5RBofXQfyfxkLwXj7/bNcXXhEs/fsET363Az9vM7Pu7k7P5uEXt3Xyb9gxB3o+BCENL35eCOFWpMSEMKx9C4Iug0635m248BO+1Wbnv8v28unvcXRqFMKH4zpTL9i/6LaWv2TMHu7zuFNDFkI4hiQCAfGbIW4NDH4FvAuf3DVJZ3N4aPZf/BmXwu1XNOaFYW3w8Srm9lLsGuNew+BXwD/U6aELISpOEoEwRgr5h15Y/iGvz3//qbPcNvd30rIsvHNjFKM7RxTfjt0Ov71o3Gfodq+TgxZCOIrM8PF0J3bC/mXQ40HwDcrfrPPuDby6eDd+3mZ+eKBXyUkAYNcCOLHduEHs7efEoIUQjiSJwNP9/jb41oRuE/I3ZeZambbyIABRDYNZ9FBvWterWXI71lxY+Qpc1g463OjMiIUQDiaJwJMl7YPdPxpJwD8EgNikDEZ9sIF1B08D8OiglgT7l2FRmi0zIPWwMXnMVP51C4QQriOJwJOt+z/j5nCPBwD4ZddJrnt/PYlns3lhWFugjOWBstNgzX+hSV9oPsiJAQshnEFuFnuqlDjYOQ+6T8TqF8bby/by0epDdIgI5qNbu9AgfYexny56ZvEF1r8HWSkw+N/5N5mFEFWHJAJPtf49MJlJ6XgfD83YxIZDydzcrRFTrm2Dn7cZ0st4Qk8/Dhs/hHZjoH4n58YshHAKSQSeKO0YbP+GpBY3MOKLg6Scy+XNMR24MbqoWcClXBGsfgPsVhj0L6eEKoRwPkkEHkhvmIa227jh7+54BSu+v78n7RoUWke4LF08iXth2yzodh+ERjolViGE80ki8DBZqScxb/qCHy29iGzemndv6khIgE/pLyzKipfBJwj6PunYIIUQlUpGDXmQI8nnWPTx83jZc8ns9jAz7uhaehIormfoyAbYtxR6TYLAWg6PVQhReeSKwEOs2HOKF79dxy8sIanxNdwxYkgpryiha0hro5REjXr5Q0+FEFWXJIJqzmbXvLt8P/9beZBXQ1cQlJVF0DXPlaOFIi4J9iyChM0w4n/gE+CwWIUQriGJoBpLPZfLI3O38fuB09zWKYxxcUug1TVlWzGsuJvFNgssfxlqXw5Rtzg2YCGES0giqKZ2Jpzh/ll/kXQ2h/+Mbs9Yyw+w5wz0eaJ8DRWeUPbXl5ByCG6eC2b59RGiOpCbxdXQ3E1HGfPRRgC+m3gFYzvVhg3vQ9MBENGljK0UcUWQkwGr/wuNekLLqx0XsBDCpeQjXTWSbbHx4o8xzNuSQJ8W4bw3thNhgT7w53Q4lwh9Z15CqwWuCDa+b7QzdraUkhCiGpFEUE3Ep2Ry/zdbiTmWzsMDmzP5ypaYTcooD73+XWh0BUT2KnuDhc/zGYmwfhq0HgENuzoydCGEi0kiqAZW70tk8rfbsdk1n90ezZVtLvvnyR1zIP0YjJh2aY2fv0ew+j9gzYZBUyoesBDCrUgiqMLsds3/Vh7k3RX7aXVZDT6+tQuR4YH/7GCzGqWm63eCZuUtD13gkuD0Qdg6E6LvhPDmjghdCOFGJBFUUWmZFiZ/u41V+5IY3akBr41qj79PoQVhdi2A1DgY8k0F+vS1UUrC2x/6PV3huIUQ7kcSQRUUcyyN+7/Zysm0bF4Z2Y5buzdCFT7R2+3GMpR12hhzB8rrfHsJm40JZP2fhaA6FQ9eCOF2JBFUMd9tieeFhTGEBfrw7X1X0LlRaNE77l0MSXvh+s/LuMzYP7Tdhi07Cy9Ab/wAAuugrnio4sELIdySJIIqIsdq46VFu5mz6Sg9m9Vi2s2dCA/yLXpnrWHtWxDWDNqOKtdxtNbkpCaTE3+YYEBZs8nt8STeXv4lVR8SQlRhkgiqgGNnsnhg1lZ2JKRxf/9mPD64JV7mEj7lH1wOJ3fCdR+UeyF5W042mcfjOf8qW1AEGeG9qZmTjVdAYImvFUJUTZII3Ny6A6d5eM5fWG2aT27rwlVt65b8gvNXA8ENocNN5T6ettlAa+x+oWizH5nt7wOTF3ab9RLfgRDC3UkicFN2u+ajNYd4+9d9NK8TxMe3dqFp7aDSX3h4HcT/CddMBbN3uY9r9vHB5O2DndqkXrvIuKIwmTD5FNMNJYSo8iQRuKG0LAuPz9vO8j2JjIiqz3+ub0+ATxn/q9a+BUGXQafbLunYJm8fgho341x8HLacbEzePgQ2jMTL1++S2hNCuD9JBG5mz4l0Js7ayrHULKZc24bxPSMvHhpanPjNELcGhrwK3pd+4vYKCKRGs8uxWy2YzF6YvMt/ZSGEqDokEbiRH7Yl8OyCv6np583ce3sQHRlWvgZ+nwr+YdDlzgrHYvLywuQlvx5CeAL5S3cTWmt+iTlFh4gQ3r+lE3VqlPMT/YmdsH8ZDHgBfMtwL0EIIfI4dT0CpdTVSql9SqmDSqlnStivq1LKppQa48x43JlSinduiuKbe7qXPwmAMYvYtyZ0m+D44IQQ1ZrTEoFSygx8AAwF2gA3K6XaFLPff4FfnBVLVRHg44V3SfMDipCddpbsg1vQu3+EbveCf4hzghNCVFvO7BrqBhzUWscCKKXmAtcBuwvt9zDwPSBF7svBmpPL6ZhDJKzZSuNzM/Ex+ZDT5nZkbI8Qoryc2TXUAIgv8Dghb1s+pVQDYBTwcUkNKaXuVUptUUptSUpKcnigVVHG8SSO/PYHXtknCLdt5ZS6gsR9SejCawwLIUQpnJkIihrzWPgs9S7wtNbaVlJDWuvpWutorXV07dq1HRVf1WS3w8m/4Y+PaJE7g3a576AxccJrAMm7YrFmZbs6QiFEFePMrqEEoGGBxxHA8UL7RANz88bJhwPXKKWsWuuFToyrarHbIXGXMWP48Do4sh6yUgkBslUtUsztSTZFY1HBBNcOkTH/Qohyc2Yi2Ay0UEo1AY4BY4FbCu6gtW5y/t9KqZnAYo9PAoVP/IfXQfYZ47nQJnD5cIjsQ3ZYRw4s30fmqWQATN5eNOjVEbO3jAgWQpSP084aWmurUuohjNFAZmCG1nqXUmpi3vMl3hfwGHY7nIr559N+4RN/62shso+x8HxwRP7L/IBWYyLITEzBZrEREB6Cf3iIK96BEKKKU1Xt5mJ0dLTesmWLq8O4dAVP/OdP/gVP/JG9izzxCyFERSiltmqto4t6TvoRHCDpaCLH9x/DarFSv0UD6jar9099oNJO/MV84hdCiMoiiaCCEg+f4uvnZpB1NgsALy/F+CeuoK7pUIETf5qxc1hTaDPCOPE37gXBDUpoWQghKockggo6+NcB7Jln6BR+gObBcTQKOob/b+8aT4Y1hTbXyYlfCOHWJBFcKpsVDq2kyf636dZhM14mG6k5wexNbc6ZgHb0fvJ5vOs0dnWUQghRKkkE5XXyb9gxF3bOg3OJ1PEJYevpduxMbs3JzDqAYvgjIyUJCCGqDEkEZXH2FPz9HeyYY9z4NXlDq6sh6mZsDfsS+Ndhcr5eQQ0/C1eM6UOLbq1cHbEQQpSZJILiWLJg31LYPgcOrQRtgwbRxlrA7a6HAGPRGB+gbZ/2NIlqhrbbCQyRtQCEEFWLJIKCtIajf8CO2bBrIeSkQ80I6D0ZOoyF2i2LfWlAzYBKC1MIIRzJYxKB3W4nOyuHgED/i59MiYOd3xpdP6mHwTvQGO0TNdYY8WNy6vo9QgjhUh6RCA7si+W7WT+y9c+dDBral+GjhtCobpDxqX/HXDi6AVDQtB/0f9ao5yPLPQohPES1TwQnjyfy8F3PcjzhJGaluSxjO92Ov0/D4ASUNRvCW8KgKdDhRpnZK4TwSNU+EcQdOsLxhJP0jUhjSq94agdYOZNt5kzj0YQOvB/qdwZV1NIJQgjhGap9IvDKK8t8PMOHnUmBLDoYyvrjIcy983lCGzQp5dVCCFH9VftE0KxFJJ26dmDb5p08utI48Y+9YzQNI6XcgxBCgAckgrBaobz+znP8ueEvdu/cS/QVnYjuHoWvr4+rQxNCCLcg6xEIIYQHKGk9AhkgL4QQHk4SgRBCeDhJBEII4eEkEQghhIeTRCCEEB5OEoEQQni4Kjd8VCmVBBxxQFPhwGkHtONoElf5SFzlI3GVT3WKq7HWunZRT1S5ROAoSqktxY2pdSWJq3wkrvKRuMrHU+KSriEhhPBwkgiEEMLDeXIimO7qAIohcZWPxFU+Elf5eERcHnuPQAghhMGTrwiEEEIgiUAIITyeRyQCpdQMpVSiUiqmwLYwpdRvSqkDed9DKzmmhkqpVUqpPUqpXUqpSW4Sl59SapNSakdeXC+7Q1wF4jMrpbYppRa7WVyHlVJ/K6W2K6W2uEtsSqkQpdR8pdTevN+1K1wdl1KqVd7P6fxXulJqsqvjyovt0bzf+xil1Jy8vwd3iGtSXky7lFKT87Y5LC6PSATATODqQtueAVZorVsAK/IeVyYr8LjWujXQA3hQKdXGDeLKAQZqraOAjsDVSqkebhDXeZOAPQUeu0tcAAO01h0LjO92h9jeA5ZprS8HojB+di6NS2u9L+/n1BHoAmQCP7g6LqVUA+ARIFpr3Q4wA2PdIK52wASgG8b/4XClVAuHxqW19ogvIBKIKfB4H1Av79/1gH0uju9HYLA7xQUEAH8B3d0hLiAi7xd+ILDYnf4fgcNAeKFtLo0NqAnEkTcoxF3iKhTLEGC9O8QFNADigTCM1RsX58Xn6rhuAD4r8PhfwFOOjMtTrgiKcpnW+gRA3vc6rgpEKRUJdAL+dIe48rpftgOJwG9aa7eIC3gX4w/AXmCbO8QFoIFflVJblVL3uklsTYEk4Iu87rTPlFKBbhBXQWOBOXn/dmlcWutjwFTgKHACSNNa/+rquIAYoK9SqpZSKgC4BmjoyLg8ORG4BaVUEPA9MFlrne7qeAC01jZtXLZHAN3yLk1dSik1HEjUWm91dSzF6KW17gwMxejm6+vqgDA+1XYGPtJadwLO4dquswsopXyAEcB3ro4FIK+P/TqgCVAfCFRK3eraqEBrvQf4L/AbsAzYgdG17DCenAhOKaXqAeR9T6zsAJRS3hhJ4But9QJ3ies8rfUZYDXG/RVXx9ULGKGUOgzMBQYqpWa5QVwAaK2P531PxOjv7uYGsSUACXlXdADzMRKDq+M6byjwl9b6VN5jV8d1JRCntU7SWluABUBPN4gLrfXnWuvOWuu+QApwwJFxeXIiWATckffvOzD66CuNUkoBnwN7tNbvuFFctZVSIXn/9sf449jr6ri01s9qrSO01pEY3Qkrtda3ujouAKVUoFKqxvl/Y/Qrx7g6Nq31SSBeKdUqb9MgYLer4yrgZv7pFgLXx3UU6KGUCsj7+xyEcXPd1XGhlKqT970RMBrj5+a4uCrzpoervvJ+aCcAC8anpLuBWhg3Hg/kfQ+r5Jh6Y/Qr7wS2531d4wZxdQC25cUVA7yYt92lcRWKsT//3Cx2eVwYffE78r52Ac+7UWwdgS15/58LgVA3iSsASAaCC2xzh7hexvjgEwN8Dfi6SVy/YyTxHcAgR/+8pMSEEEJ4OE/uGhJCCIEkAiGE8HiSCIQQwsNJIhBCCA8niUAIITycJAIhSqCUsuVVyIxRSn2XN8W/qP02VHZsQjiKJAIhSpaljUqZ7YBcYGLBJ5VSZgCtdU9XBCeEI0giEKLsfgeaK6X6K2MtidnA3wBKqYzzOymlnspbm2CHUuo/eduaKaWW5RWl+10pdblr3oIQF/NydQBCVAVKKS+M2jjL8jZ1A9ppreMK7TcUGAl011pnKqXC8p6aDkzUWh9QSnUHPsQopy2Ey0kiEKJk/nklucG4IvgcoxDZpsJJIM+VwBda60wArXVKXoXZnsB3RgkbwChdIIRbkEQgRMmytFGSO1/eyfxcMfsrjBpSBZmAM4XbEcJdyD0CIRzrV+Cu86OLlFJh2lhnIk4pdUPeNqWUinJlkEIUJIlACAfSWi/DKA+8Ja9L6Ym8p8YBdyulzlcovc41EQpxMak+KoQQHk6uCIQQwsNJIhBCCA8niUAIITycJAIhhPBwkgiEEMLDSSIQQggPJ4lACCE83P8DN5AsaCE1X3QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve_conv = (df_cop\n",
    "                      .query(\"conv==1\")\n",
    "                      [[f\"claim_{p}\" for p in df_cop[\"price\"].unique()]]\n",
    "                      .melt()\n",
    "                      .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[-1].astype(int))\n",
    "                      .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "\n",
    "ortho_df = orthogonalize(orthogonalize(df_cop, x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"claim\").query(\"conv==1\")\n",
    "\n",
    "factual_claim_curve = (ortho_df\n",
    "                       .groupby(\"price_deb\")\n",
    "                       .apply(lambda d: np.average(d[\"claim_deb\"])))\n",
    "\n",
    "plt.plot(cf_claim_curve_conv, label=\"P(claim(price) | conv=1)\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim | price, conv=1)\")\n",
    "plt.legend()\n",
    "\n",
    "sns.scatterplot(data=(ortho_df\n",
    "                      .groupby([\"price_deb\", \"model\"])[\"claim_deb\"]\n",
    "                      .mean().reset_index()),\n",
    "                x=\"price_deb\", y=\"claim_deb\", hue=\"model\")\n",
    "\n",
    "plt.ylabel(\"Claim\")\n",
    "plt.xlabel(\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "66b727a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:27.720177Z",
     "start_time": "2023-07-13T18:15:27.568923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factual_conv_curve = (orthogonalize(orthogonalize(df_cop, x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"conv\")\n",
    "                      .groupby(\"price_deb\")\n",
    "                      .apply(lambda d: np.average(d[\"conv_deb\"])))\n",
    "\n",
    "\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "plt.plot(pd.DataFrame(factual_conv_curve.values*factual_claim_curve.values, index=factual_claim_curve.index),\n",
    "         label=\"P(conv|price)*P(claim|price)\")\n",
    "\n",
    "\n",
    "ortho_df = orthogonalize(orthogonalize(df_cop, x=[\"model\"], t=\"price\"), x=[\"model\"], t=\"claim\")\n",
    "sns.scatterplot(data=(ortho_df\n",
    "                      .groupby([\"price_deb\", \"model\"])[\"claim_deb\"]\n",
    "                      .mean().reset_index()),\n",
    "                x=\"price_deb\", y=\"claim_deb\", hue=\"model\")\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75569d2",
   "metadata": {},
   "source": [
    "# Adding an Approve Rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9da1d665",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:27.726385Z",
     "start_time": "2023-07-13T18:15:27.721692Z"
    }
   },
   "outputs": [],
   "source": [
    "price_assignment = pd.DataFrame(dict(\n",
    "    price = [20,40,60,80,   20,40,60,80, 20,40,60,80, 20,40,60,80],\n",
    "    ps =    [.4,.3,.2,.1,  .3,.3,.2,.2, .2,.2,.3,.3, .1,.2,.3,.4,],\n",
    "    model = [1,1,1,1,       2,2,2,2,     3,3,3,3,     4,4,4,4,]\n",
    ")).set_index(\"model\")\n",
    "\n",
    "\n",
    "approval_rule = pd.DataFrame(dict(\n",
    "    ps =    [.8,.6,.4,.2],\n",
    "    model = [1,2,3,4]\n",
    ")).set_index(\"model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "40e5d2a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:40.750287Z",
     "start_time": "2023-07-13T18:15:27.727809Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "health                5.503251\n",
      "time                  6.503860\n",
      "price                49.468200\n",
      "conv                  0.838120\n",
      "approved_ps           0.517190\n",
      "approved              0.517070\n",
      "model                 2.414050\n",
      "claim                 0.281780\n",
      "conv_20               0.915040\n",
      "conv_40               0.867930\n",
      "conv_60               0.809580\n",
      "conv_80               0.741680\n",
      "claim_20_non_appr     0.350760\n",
      "claim_40_non_appr     0.592320\n",
      "claim_60_non_appr     0.774160\n",
      "claim_80_non_appr     0.741680\n",
      "claim_20              0.157670\n",
      "claim_40              0.279460\n",
      "claim_60              0.381600\n",
      "claim_80              0.368900\n",
      "appr_w                2.379942\n",
      "ps                    0.275542\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>health</th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "      <th>conv</th>\n",
       "      <th>approved_ps</th>\n",
       "      <th>approved</th>\n",
       "      <th>model</th>\n",
       "      <th>claim</th>\n",
       "      <th>conv_20</th>\n",
       "      <th>conv_40</th>\n",
       "      <th>...</th>\n",
       "      <th>claim_20_non_appr</th>\n",
       "      <th>claim_40_non_appr</th>\n",
       "      <th>claim_60_non_appr</th>\n",
       "      <th>claim_80_non_appr</th>\n",
       "      <th>claim_20</th>\n",
       "      <th>claim_40</th>\n",
       "      <th>claim_60</th>\n",
       "      <th>claim_80</th>\n",
       "      <th>appr_w</th>\n",
       "      <th>ps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.736504</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.991294</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.492579</td>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.000869</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.968288</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     health  time  price  conv  approved_ps  approved  model  claim  conv_20  \\\n",
       "0  1.736504     3     60     0          0.8         1      1      0        0   \n",
       "1  2.991294    12     60     1          0.8         1      1      1        1   \n",
       "2  2.492579     9     60     1          0.8         1      1      1        1   \n",
       "3  2.000869    10     60     1          0.8         0      1      0        1   \n",
       "4  1.968288     2     60     0          0.8         1      1      0        0   \n",
       "\n",
       "   conv_40  ...  claim_20_non_appr  claim_40_non_appr  claim_60_non_appr  \\\n",
       "0        0  ...                  0                  0                  0   \n",
       "1        1  ...                  0                  1                  1   \n",
       "2        1  ...                  0                  0                  1   \n",
       "3        1  ...                  0                  0                  1   \n",
       "4        0  ...                  0                  0                  0   \n",
       "\n",
       "   claim_80_non_appr  claim_20  claim_40  claim_60  claim_80  appr_w   ps  \n",
       "0                  0         0         0         0         0    1.25  0.2  \n",
       "1                  1         0         1         1         1    1.25  0.2  \n",
       "2                  1         0         0         1         1    1.25  0.2  \n",
       "3                  1         0         0         0         0    1.25  0.2  \n",
       "4                  0         0         0         0         0    1.25  0.2  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 100000\n",
    "\n",
    "np.random.seed(123)\n",
    "\n",
    "health = np.random.beta(2,2, n)*11\n",
    "model = (np.round(np.random.beta(health/health.mean(), 1)*3)+1).astype(int)\n",
    "time = np.random.randint(1, 13, n)\n",
    "\n",
    "price = np.array([np.random.choice(price_assignment.loc[m][\"price\"], p=price_assignment.loc[m][\"ps\"])\n",
    "                  for m in model])\n",
    "\n",
    "appr_chance = approval_rule.loc[model].values.flatten()\n",
    "approved = np.random.binomial(1, p=appr_chance)\n",
    "\n",
    "def conv_fn(p):\n",
    "    return ((-0.05*p + health + time) > 5)*1\n",
    "\n",
    "\n",
    "def claim_fn(p):\n",
    "    return ((health + 0.1*p + 0.5*time) > 12)*1\n",
    "\n",
    "\n",
    "conv = conv_fn(price)\n",
    "claim = claim_fn(price)\n",
    "\n",
    "df_cop = pd.DataFrame(dict(\n",
    "    health=health,\n",
    "    time=time,\n",
    "    price=price,\n",
    "    conv=conv,\n",
    "    approved_ps = appr_chance,\n",
    "    approved=approved,\n",
    "    model=model,\n",
    "    claim=claim*conv*approved,\n",
    "    **{**{f\"conv_{p}\": conv_fn(p) for p in np.unique(price)}}\n",
    "))\n",
    "\n",
    "\n",
    "df_cop = (df_cop\n",
    "          .assign(**{f\"claim_{p}_non_appr\": df_cop[f\"conv_{p}\"]*claim_fn(p) for p in np.unique(price)})\n",
    "          .assign(**{f\"claim_{p}\": df_cop[f\"conv_{p}\"]*claim_fn(p)*approved for p in np.unique(price)})\n",
    "          .assign(**{\"appr_w\": 1/df_cop[\"approved_ps\"]})\n",
    "          .merge(price_assignment.reset_index(), on=[\"price\", \"model\"]))\n",
    "\n",
    "print(df_cop.mean())\n",
    "df_cop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcedd01e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T16:51:54.681470Z",
     "start_time": "2023-07-13T16:51:54.656744Z"
    }
   },
   "source": [
    "## Estimate Risk for Converted Non-Approved "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "65acb0b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:41.336432Z",
     "start_time": "2023-07-13T18:15:40.751771Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_claim_curve_conv = (df_cop\n",
    "                      .query(\"conv==1\")\n",
    "                      [[f\"claim_{p}_non_appr\" for p in df_cop[\"price\"].unique()]]\n",
    "                      .melt()\n",
    "                      .assign(price=lambda d: d[\"variable\"].str.split(\"_\").str[1].astype(int))\n",
    "                      .groupby([\"price\"])[[\"value\"]].mean())\n",
    "\n",
    "factual_claim_curve_appr = (df_cop\n",
    "                            .query(\"conv==1\")\n",
    "                            .query(\"approved==1\")\n",
    "                            .groupby(\"price\")\n",
    "                            .apply(lambda d: np.average(d[\"claim\"],  weights=1/(d[\"ps\"]))))\n",
    "\n",
    "factual_claim_curve = (df_cop\n",
    "                       .query(\"conv==1\")\n",
    "                       .query(\"approved==1\")\n",
    "                       .groupby(\"price\")\n",
    "                       .apply(lambda d: np.average(d[\"claim\"],  weights=d[\"appr_w\"]/(d[\"ps\"]))))\n",
    "\n",
    "plt.plot(cf_claim_curve_conv, label=\"P(claim(price) | conv=1)\")\n",
    "plt.plot(factual_claim_curve, label=\"P(claim | price, conv=1)\")\n",
    "plt.plot(factual_claim_curve_appr, label=\"P(claim | price, conv=1, appr=1)\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Claim\")\n",
    "plt.xlabel(\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e72cdc57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-13T18:15:41.426207Z",
     "start_time": "2023-07-13T18:15:41.338466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factual_conv_curve = (df_cop\n",
    "                      .groupby(\"price\")\n",
    "                      .apply(lambda d: np.average(d[\"conv\"], weights=1/d[\"ps\"])))\n",
    "\n",
    "plt.plot(pd.DataFrame(factual_conv_curve.values*factual_claim_curve.values, index=factual_claim_curve.index),\n",
    "         label=\"P(conv|price)*P(claim|price)\")\n",
    "\n",
    "plt.plot(cf_claim_curve, label=\"P(claim(price))\")\n",
    "\n",
    "plt.plot(pd.DataFrame(factual_conv_curve.values*factual_claim_curve_appr.values, index=factual_claim_curve_appr.index),\n",
    "         label=\"P(conv|price)*P(claim|price, appr=1)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d60b5ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44c999b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0155b7f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "686ef3e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bf0fb77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8492d5b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T13:30:10.500964Z",
     "start_time": "2023-07-14T13:30:09.535587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lusa</th>\n",
       "      <th>lines</th>\n",
       "      <th>spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2000</td>\n",
       "      <td>299.970032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4000</td>\n",
       "      <td>2600.974724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3000</td>\n",
       "      <td>1202.090258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>-600.611310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>2200.610939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>-599.246336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>4</td>\n",
       "      <td>3000</td>\n",
       "      <td>701.379035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1099.082185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>2</td>\n",
       "      <td>4000</td>\n",
       "      <td>2601.263860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>3</td>\n",
       "      <td>2000</td>\n",
       "      <td>299.523103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       lusa  lines        spend\n",
       "0         3   2000   299.970032\n",
       "1         2   4000  2600.974724\n",
       "2         3   3000  1202.090258\n",
       "3         3   1000  -600.611310\n",
       "4         1   3000  2200.610939\n",
       "...     ...    ...          ...\n",
       "19995     3   1000  -599.246336\n",
       "19996     4   3000   701.379035\n",
       "19997     4   1000 -1099.082185\n",
       "19998     2   4000  2601.263860\n",
       "19999     3   2000   299.523103\n",
       "\n",
       "[20000 rows x 3 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 20000\n",
    "\n",
    "possible_lines = [1000, 2000, 3000, 4000]\n",
    "\n",
    "lines_ps = pd.DataFrame(dict(lusa = [1,1,1,1,      2,2,2,2,     3,3,3,3,     4,4,4,4],\n",
    "                             ps =   [.0,.1,.2,.7, .1,.2,.3,.4, .4,.3,.2,.1, .7,.2,.1,.0])).set_index(\"lusa\")\n",
    "\n",
    "\n",
    "np.random.seed(123)\n",
    "\n",
    "lusas = np.random.choice([1,2,3, 4], size=n)\n",
    "lines = np.array([np.random.choice(possible_lines, p=lines_ps.loc[lusa].values.flatten()) for lusa in lusas])\n",
    "spend = np.random.normal(0.9*lines - 500*lusas)\n",
    "\n",
    "df = pd.DataFrame(dict(lusa = lusas, lines=lines, spend=spend))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "180de36d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T13:30:12.917552Z",
     "start_time": "2023-07-14T13:30:12.475293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lines', ylabel='spend'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x=\"lines\", y=\"spend\", hue=\"lusa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "af01953d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T13:32:42.408173Z",
     "start_time": "2023-07-14T13:32:41.939440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lines_deb', ylabel='spend'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df.assign(lines_deb = df[\"lines\"].mean() + df[\"lines\"] - df.groupby(\"lusa\")[\"lines\"].transform(\"mean\"),\n",
    "                               spend_deb = df[\"spend\"].mean() + df[\"spend\"] - df.groupby(\"lusa\")[\"spend\"].transform(\"mean\")\n",
    "                              ),\n",
    "                x=\"lines_deb\", y=\"spend\", hue=\"lusa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "597a9e59",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T13:34:05.179472Z",
     "start_time": "2023-07-14T13:34:04.712387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lines_deb', ylabel='spend_deb'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df.assign(lines_deb = df[\"lines\"].mean() + df[\"lines\"] - df.groupby(\"lusa\")[\"lines\"].transform(\"mean\"),\n",
    "                               spend_deb = df[\"spend\"].mean() + df[\"spend\"] - df.groupby(\"lusa\")[\"spend\"].transform(\"mean\")\n",
    "                              ),\n",
    "                x=\"lines_deb\", y=\"spend_deb\", hue=\"lusa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "258d6e72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c86b02e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}