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- Дата: 25-03-2023, 15:10
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![Causal Inference in Python: Applying Causal Inference in the Tech Industry (3rd Early Release)](/uploads/posts/2023-03/1679746308_3991_causal_inf_r_nc__in_python__3rd__arly_r_l_as__.jpg)
Автор: Matheus Facurer
Издательство: O’Reilly Media, Inc.
Год: 2023-03-24
Страниц: 399
Язык: английский
Формат: epub
Размер: 10.2 MB
This book is an introduction to Causal Inference in Python, but it is not an introductory book in general. It’s introductory because I’ll focus on application, rather than rigorous proofs and theorems of causal inference; additionally, when forced to choose, I’ll opt for a simpler and intuitive explanation, rather than a complete and complex one. It is not introductory in general because I’ll assume some prior knowledge about Machine Learning (ML), statistics and programming in Python. It is not too advanced either, but I will be throwing in some terms that you should know beforehand. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example.