Python Causal Inference Book for The Brave and True

[chengjun]

GitHub - matheusfacure/python-causality-handbook: Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.

勇敢和真实的因果推理。轻松而严谨的方法来学习影响评估和敏感性分析。

Causal Inference for the Brave and True 这本书由巴西Nubank的Staff Data Scientist Matheus Facure 所著。该书用平实的语言和严谨的数学，以及实用的Python代码，结合经济学与社会学的策略评估和敏感性分析应用，对因果推断最新的概念、理论及实践进行了非常全面的介绍，既适合初学者入门，同时也适合技术管理专家回顾相关领域的整体知识。该书英文原版的Jupyter Notebooks可以由该Github地址获取。

本书主要基础是计量经济学，吸收了非常多学者，包括 Joshua Angrist, Alberto Abadie，Christopher Walters，Miguel Hernan 和 Jamie Robins 等，在这方面的最新研究，主要参考了以下资料：

• Cross-Section Econometrics
• Mastering Mostly Harmless Econometrics
• Mostly Harmless Econometrics
• Mastering 'Metrics
• Causal Inference Book

Causal Inference for The Brave and True — Causal Inference for the Brave and True

• Causal Inference for The Brave and True

PART I - THE YANG

• 01 - Introduction To Causality
• 02 - Randomised Experiments
• 03 - Stats Review: The Most Dangerous Equation
• 04 - Graphical Causal Models
• 05 - The Unreasonable Effectiveness of Linear Regression
• 06 - Grouped and Dummy Regression
• 07 - Beyond Confounders
• 08 - Instrumental Variables
• 09 - Non Compliance and LATE
• 10 - Matching
• 11 - Propensity Score
• 12 - Doubly Robust Estimation
• 13 - Difference-in-Differences
• 14 - Panel Data and Fixed Effects
• 15 - Synthetic Control
• 16 - Regression Discontinuity Design

PART II - THE YIN

• 17 - Predictive Models 101
• 18 - Heterogeneous Treatment Effects and Personalization
• 19 - Evaluating Causal Models
• 20 - Plug-and-Play Estimators
• 21 - Meta Learners
• 22 - Debiased/Orthogonal Machine Learning
• 23 - Challenges with Effect Heterogeneity and Nonlinearity
• 24 - The Difference-in-Differences Saga

APPENDIX

• Debiasing with Orthogonalization
• Debiasing with Propensity Score
• When Prediction Fails
• Why Prediction Metrics are Dangerous For Causal Models

CONTRIBUTE

中文版

GitHub - xieliaing/CausalInferenceIntro: Causal Inference for the Brave and True的中文翻译版。全部代码基于Python，适用于计量经济学、量化社会学、策略评估等领域。英文版原作者：Matheus Facure

https://gitee.com/xieliaing/causal-inference-intro-gitee/tree/master/chapters