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.
If you want to read the book in Brazilian Portuguese, @rdemarqui made this awesome translation: Inferência Causal para os Corajosos e Verdadeiros
If you want to read the book in Chinese, @xieliaing was very kind to make a translation: 因果推断：从概念到实践
If you want to read the book in Spanish, @donelianc was very kind to make a translation: Inferencia Causal para los Valientes y Verdaderos
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
Also, some really kind folks (@vietecon, @dinhtrang24 and @anhpham52) also translated this content into Vietnamese: Nhân quả Python
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.
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.
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 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, which provides more insights into applying causal inference in the industry.
