---
title: "Causal Inference for The Brave and True"
source: Causal Inference for the Brave and True
source_url: https://github.com/matheusfacure/python-causality-handbook
licence: MIT
domain: data-and-analysis
subdomain: causal-inference-handbook
date_added: 2026-04-25
---

# Causal Inference for The Brave and True

![img](./causal-inference-for-the-brave-and-true/data/img/brave-and-true.png)


A light-hearted yet rigorous approach to learning impact estimation and sensitivity analysis. All in Python and with as many memes as I could find.

[Check out the book here!](https://matheusfacure.github.io/python-causality-handbook/landing-page.html)

If you want to read the book in Brazilian Portuguese, @rdemarqui made this awesome translation:  
[Inferência Causal para os Corajosos e Verdadeiros](https://github.com/rdemarqui/python-causality-handbook-ptbr)

If you want to read the book in French, Arthur Mello put a lot of effort into this beautiful translation:  
[L'Inférence Causale pour les Courageux et les Vrais](https://github.com/arthurmello/python-causality-handbook)

If you want to read the book in Chinese, @xieliaing was very kind to make a translation (Chapters 1-21):  
[因果推断：从概念到实践](https://github.com/xieliaing/CausalInferenceIntro)

There is also a more recent Chinese tralsation by 黄文喆（Wenzhe Huang) and 许文立（Wenli Xu) (All Chapters):  
[因果推断：献给求真敢为者](https://github.com/Wenzhe-Huang/python-causality-handbook-zh)

If you want to read the book in Spanish, @donelianc was very kind to make a translation:  
[Inferencia Causal para los Valientes y Verdaderos](https://github.com/donelianc/introduccion-inferencia-causal)

If you want to read it in Korean, @jsshin2019 has put up a team to make the that translation possible:  
[Python으로 하는 인과추론 : 개념부터 실습까지](https://github.com/TeamCausality/Causal-Inference-with-Python)

Also, some really kind folks (@vietecon, @dinhtrang24 and @anhpham52) also translated this content into Vietnamese:  
[Nhân quả Python](https://github.com/vietecon/NhanQuaPython)


I like to think of this entire series as a tribute to Joshua Angrist, Alberto Abadie and Christopher Walters for their amazing Econometrics class. Most of the ideas here are taken from their classes at the American Economic Association. Watching them is what is keeping me sane during this tough year of 2020.
* [Cross-Section Econometrics](https://www.aeaweb.org/conference/cont-ed/2017-webcasts)
* [Mastering Mostly Harmless Econometrics](https://www.aeaweb.org/conference/cont-ed/2020-webcasts)

I'd also like to reference the amazing books from Angrist. They have shown me that Econometrics, or 'Metrics as they call it, is not only extremely useful but also profoundly fun.

* [Mostly Harmless Econometrics](https://www.mostlyharmlesseconometrics.com/)
* [Mastering 'Metrics](https://www.masteringmetrics.com/)

Finally, I'd like to reference Miguel Hernan and Jamie Robins' book. It has been my trustworthy companion in the most thorny causal inference questions I've had to answer.

* [Causal Inference Book](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)

## How to Support This Work

Causal Inference for the Brave and True is an open-source resource primarily focused on econometrics and the statistics of science. It exclusively utilizes free software, grounded in Python. The primary objective is to ensure accessibility, not only from a financial standpoint but also from an intellectual perspective. I've tried my best to keep the content entertaining while maintaining the necessary scientific rigor.


If you want to show your appreciation for this work, consider  going to https://www.patreon.com/causal_inference_for_the_brave_and_true. Alternatively, you can purchase my book, [Causal Inference in Python](https://www.amazon.com/Causal-Inference-Python-Applying-Industry/dp/1098140257), which provides more insights into applying causal inference in the industry.
