1. 作为因果推断的基础——多元回归
2. 基本假定与计量模型的脉络及发展
3. 计量建模原则与分析策略
4. 案例与Stata操作

1. 内生性问题与解决路径
2. 工具变量选择的方法及分类
3. 工具变量的检验
4. 工具变量回归模型（2SLS+GMM）
5. 案例与Stata操作

1. 回归模型与因果解释
2. 尤尔-辛普森悖论
3. Rubin因果模型（Rubin Causal Model, RCM）
4. 决策理论因果模型（decision-theoretic causality）
5. 因果图（Causal Diagram: DAGs）
6. 格兰杰因果关系检验（Granger causality test）
7. 双重差分估计（difference in differences estimate, DID）
8. 倾向值匹配法（propensity score matching, PSM）
9. 固定效应模型（fixed effect model, FEM）
10. 随机效应模型（random effect model, REM）
11. 断点回归设计（regression discontinuity design, RDD）
12. 案例与Stata操作

1. Angrist,J. D., & Pischke, J. S. (2008). Mostly Harmless Econometrics: An Empiricist'sCompanion. Princeton university press.
2. Cameron,A. C., & Trivedi, P. K. (2005). Micro-econometrics: Methods andApplications. Cambridge university press.
3. Greene,W. H. (2011). Econometric Analysis (7th). Prentice Hall.
4. Imbens,G. W., & Rubin, D. B. (2015). Causal Inference in Statistics, Social,and Biomedical sciences. Cambridge University Press.
5. Morgan,S. L., & Winship, C. (2007). Counterfactuals and Causal Inference:Methods and Principles for Social Research. New York, NY. CambridgeUniversity Press.
6. Pearl,J. (2009). Causality: Models, Reasoning, and Inference (2nd). New York:Cambridge University Press.
7. Pearl,J., & Mackenzie, D. (2018). The Book of Why: the New Science of Causeand Effect. CITIC press.

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