https://github.com/matheusfacure/python-causality-handbook

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。

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

https://matheusfacure.github.io/python-causality-handbook/landing-page.html

• 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

https://github.com/xieliaing/CausalInferenceIntro

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