Introduction

This Julia Jupyterbook 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 notebooks were in R and we decided to translate them into Python, and Julia.

• 1. Linear Model Overfiting
• 2. OLS and lasso for wage prediction
• 3. OLS and lasso for gender wage gap inference
• 4. Some RCT Examples
• 5. Analyzing RCT reemployment experiment
• 6. Analyzing RCT reemployment experiment
• 7. Linear Penalized Regs
• 8. ML for wage prediction
• 9. Experiment on Orthogonal Learning
• 10. Double Lasso for the convergence hypothesis
• 11. Heterogenous Wage Effects
• 12. ColliderBias Hollywood
• 13. Deep Neural Networks for Wage Prediction
• 14. AutoML for wage prediction
• 15. Functional Approximations by NN and RF
• 16. Notebook-DAGitty
• 17. Notebook-Dosearch
• 18. DML inference for gun ownership
• 19. DML inference using NN for gun ownership
• 20. DML for ATE and LATE of 401(k) on Wealth
• 21. Identification Analysis of 401(k) Example w DAGs
• 22. Debiased ML for Partially Linear Model in Julia
• 23. Sensitivity Analysis with Sensmakr and Debiased ML
• 24. Debiased ML for Partially Linear IV Model in Julia
• 25. Weak IV Experiments