GitHub - d2cml-ai/14.388_py: This material has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Python, so students can manage both programing languages. Jannis Kueck and V. Chernozukhov have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey’s Machine Learning and Causal Inference course. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.

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This material has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Python, so students can manage both programing languages. Jannis Ku…

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Name		Name	Last commit message	Last commit date
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.github/workflows		.github/workflows
.ipynb_checkpoints		.ipynb_checkpoints
Figs		Figs
Python_notebooks		Python_notebooks
_build		_build
data		data
_config.yml		_config.yml
_toc.yml		_toc.yml
intro.md		intro.md
references.bib		references.bib
requirements.txt		requirements.txt