causalml-basics

This repository contains a collection of Jupyter notebooks focused on machine learning methods for causal inference. Each notebook provides applied examples using simulated and real data.

The notebooks are organized as follows:

1. OLS and Overfitting: basic functionalities of statsmodels and pyfixest for linear regression with the case of overfitting.
2. Regression with Lasso: prediction of wages using penalized linear regression.
3. Classification Models: basics of classification and model evaluation using sklearn.
4. Clustering: This notebooks explores dimensionality reduction techniques like Principal Component Analysis (PCA) and K-means clustering.
5. Imbalanced Data: techniques to handle imbalanced data using imbalanced-learn.
6. Tree-based Methods: Basic introduction to tree-based methods like Decision Trees and Random Forest.
7. Neural Networks: Basic introduction to neural networks using sklearn and pytorch.
8. Regression using ML: This notebook is based on a lab from Chapter 9 of the book Causal ML. The goal is to predict wages using non-linear models and stacking.
9. Double/Debiased ML: IN PROGRESS
10. Heterogeneous Treatment Effects: IN PROGRESS