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[KOR] Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

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[한국어] Causal Inference for The Brave and True

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Introduction

This repository serves as an unofficial Korean translation of the incredible text book on causal inference titled "Causal Inference for The Brave and True." For the official Korean translation, please refer to the provided link. I have created this repository for my personal study purposes. If you find this content valuable, kindly share it with others who may benefit from it and consider giving it a star on both the original repository and here as well. 😏

이 페이지는 "Causal Inference for The Brave and True"의 비공식 한국어 번역입니다. 공식 한국어 번역은 링크를 참고바랍니다. 이 페이지는 제 개인적인 공부를 위해 정리했습니다만 인과추론을 공부하고 싶으신 분께 도움이 되고자 공유하게 되었습니다. 도움이 된다면 좋겠습니다. 😏

Table of contents

Title ENG KOR Update
PART I
01 Introduction To Causality LINK LINK 23.05.30
02 Randomised Experiments LINK LINK 23.06.03
03 Stats Review: The Most Dangerous Equation LINK LINK 23.06.04
04 Graphical Causal Models LINK LINK 22.12.27
05 The Unreasonable Effectiveness of Linear Regression LINK LINK 22.12.27
06 Grouped and Dummy Regression LINK LINK 22.12.27
07 Beyond Confounders LINK LINK 22.12.28
08 Instrumental Variables LINK LINK 22.12.30
09 Non Compliance and LATE LINK LINK 22.12.31
10 Matching LINK LINK 23.01.05
11 Propensity Score LINK LINK 23.01.09
12 Doubly Robust Estimation LINK LINK 23.01.10
13 Difference-in-Differences LINK LINK 23.01.12
14 Panel Data and Fixed Effects LINK LINK 23.01.18
15 Synthetic Control LINK LINK 23.01.25
16 Regression Discontinuity Design LINK LINK 23.01.30
PART II
17 Predictive Models 101 LINK LINK 23.02.17
18 Heterogeneous Treatment Effects and Personalization LINK LINK 23.02.19
19 Evaluating Causal Models LINK LINK 23.02.20
20 Plug-and-Play Estimators LINK LINK 22.02.21
21 Meta Learners LINK LINK 22.02.22
22 Debiased/Orthogonal Machine Learning LINK LINK 22.11.22
23 Challenges with Effect Heterogeneity and Nonlinearity LINK LINK 23.02.23
24 The Difference-in-Differences Saga LINK LINK 23.03.02
25 Synthetic Difference-in-Differences LINK TBD
APPENDIX
26 Debiasing with Orthogonalization LINK LINK 23.03.14
27 Debiasing with Propensity Score LINK LINK 23.03.22
28 When Prediction Fails LINK TBD
29 Why Prediction Metrics are Dangerous For Causal Models LINK LINK 23.03.13
30 Conformal Inference for Synthetic Controls LINK TBD

References

  1. Causal Inference for The Brave and True
  2. python-causality-handbook, matheusfacure
  3. Python으로 하는 인과추론 : 개념부터 실습까지

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[KOR] Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

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