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Content：

Potential outcomes, theory and assumptions

Fisher's exact test, Neymans approach to completely randomized experiments and model based (Bayesian) inference

Causal effect estimators, propensity score - model building, stratification, matching and weighing

Variance estimation

Instrumental variables

Instruction：

Instruction is given in the form of in-class lectures.

Computer exercises/assignments will be distributed. Solutions are to be presented by students and discussed in the class (Discuss).

Book：

Imbens Guido W. and Donald B. Rubin (IR), Causal inference in statistics, social, and biomedical sciences, Cambridge University Press.

More information:

https://ymsc.tsinghua.edu.cn/en/info/1047/2798.htm