Python companion to the R and Stata Double LASSO tutorials — same data, same five estimators, plus a hands-on introduction to the DoubleML library (DoubleMLPLR, DoubleMLIRM, and learner-robustness across LASSO, RandomForest, XGBoost).
If a job training program helps unemployed people find work, who gains the most, and who should be offered a place first? This Python tutorial uses causal machine learning on simulated jobseeker data with known true effects to move from the average effect to personal effects and a better way to assign training. It comes with an interactive app that runs in your web browser.