不可错过！杜克大学《因果推断》课程，全面讲述因果推理

目录内容：

Chapter 1. 引言 Introduction [slides] * Chapter 2. 随机试验 Randomized experiments

Chapter 2.1: Fisher's and Neyman's mode of inference [slides] * Chapter 2.2: Covariate adjustment in RCT [slides] * Chapter 3. Observational studies with ignorable assignments: single-time treatments

Chapter 3.1. Outcome regression [slides] * Chapter 3.2. Covariate balance, matching, stratification [slides] * Chapter 3.3. Propensity score [slides] * Chapter 3.4. Propensity score weighting: inverse probability weighting and overlap weighting [slides] * Chapter 3.5. Augmented weighting and double-robust estimators [slides] * Chapter 3.6. Causal inference with multiple or continuous treatments [slides] * Chapter 4. 异质治疗效应和机器学习 Heterogenous treatment effects and machine learning [slides] * Chapter 5. 敏感性分析 Sensitivity analysis [slides] * Chapter 6. Instrumental variable and principal stratification

Chapter 6.1. Instrumental variable (IV), noncompliance in RCT [slides] * Chapter 6.2. Post-treatment confounding: Principal Stratification [slides] * Chapter 7. Regression discontinuity design (RDD) [slides] * Chapter 8. Panel data: Difference-in-differences (DID) and Synthetic control (SC) [slides] * Chapter 9. Sequentially ignorable assignments: time-varying treatments [slides] * Chapter 10. Bayesian inference for causal effects [slides]