Here you can find a series of Jupyter Notebooks, my colleages, Jesus Soto & Franco Caceres and I worked on for our class of Applied Statistics. Here we show our skills in the following Machine Learning and Causal Inference's topics:
Methods available: Data splitting, Partialling out, Cross validation, Boostraping, Bagging.
Models available: OLS (with RCT data), IRA, CRA, Lasso, Dobble lasso, Tree and Random Forest, Causal Tree & Random Forest and Debiased Machine Learning.