matheusfacure/python-causality-handbook

What it solves

This project addresses the difficulty of learning causal inference—the study of determining cause-and-effect relationships—by providing an accessible, rigorous, and entertaining educational resource. It helps bridge the gap between complex statistical theory and practical application.

How it works

The project is an open-source, web-based book that teaches impact estimation and sensitivity analysis entirely through Python. It combines academic rigor with a light-hearted tone, using examples, explanations, and memes to make challenging econometric concepts easier to grasp.

Who it’s for

It is designed for students, data scientists, and researchers who want to understand how to estimate causal effects using data. It is particularly useful for those who prefer a hands-on, code-first approach to learning statistics and econometrics.

Highlights

• Entirely Python-based practical examples.
• Focuses on impact estimation and sensitivity analysis.
• Available in multiple languages, including Portuguese, French, Chinese, Spanish, Korean, and Vietnamese.
• Maintains scientific rigor while using an entertaining, meme-friendly teaching style.