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This course aims to provide an introduction to the fundamental concepts, methods, and theoretical results related to causal inference. The course content covers the following aspects: The potential outcome framework, The randomization experiments, The observational study and propensity score, Double-robust estimation and double-machine learning, Estimation of propensity score. After that, we introduce causal inference from machine learning perspective, including the following topics: Causal graph and d-separation, do-calculus, structural causal model, and analysis of a linear structural causal model.
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