来自杜克大学Fan Li的简明《因果推理》课程！

Chapter 1. Introduction 引言 Chapter 2. Randomized experiments 随机实验 Chapter 2.1: Fisher's and Neyman's mode of inference Chapter 2.2: Covariate adjustment in RCT Chapter 3. Observational studies with ignorable assignments: single-time treatments Chapter 3.1. Outcome regression Chapter 3.2. Covariate balance, matching, stratification Chapter 3.3. Propensity score Chapter 3.4. Propensity score weighting: inverse probability weighting and overlap weighting Chapter 3.5. Augmented weighting and double-robust estimators Chapter 3.6. Causal inference with multiple or continuous treatments Chapter 4. Heterogenous treatment effects and machine learning 异构治疗效应与机器学习 Chapter 5. Sensitivity analysis 敏感性分析 Chapter 6. Instrumental variable and principal stratification Chapter 6.1. Instrumental variable (IV), noncompliance in RCT Chapter 6.2. Post-treatment confounding: Principal Stratification Chapter 7. Regression discontinuity design (RDD) Chapter 8. Panel data: Difference-in-differences (DID) and Synthetic control (SC) Chapter 9. Sequentially ignorable assignments: time-varying treatments Chapter 10. 贝叶斯推断因果效应，Bayesian inference for causal effects