.ipynb .md .pdf

repository open issue suggest edit

Binder Live Code

Machine Learning-Based Causal Inference

This JupyterBook has been created based on the tutorials of the course MGTECON 634 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

Previous website

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

Who is the book for? :class: important

You can find all these Python scripts in this repository.

next

1. Introduction