Machine Learning-Based Causal Inference

This Python JupyterBook has been created based on the tutorials of the course “MGTECON 634: Machine Learning and Causal Inference” at Stanford taught by Professor Susan Athey. All the scripts were in R-markdown and we decided to translate each of them into Python, so students can manage both programing languages. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.

Topics

• 1. Introduction
• 2. Introduction to Machine Learning
• 3. ATE I: Binary treatment
• 4. HTE I: Binary treatment
• 5. Policy Evaluation I - Binary Treatment
• 6. Policy Learning I - Binary Treatment
• 7. Tutorial to simulate data using WGANs
• 8. Athey, S., Chetty, R., Imbens, G. W., & Kang, H. (2019). The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely (No. w26463). National Bureau of Economic Research.

Incoming website

This JupyterBook will receive future updates. It’s a draft. Not ready for publication.

Repository

You can find all of these Python scripts in this repository.