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OIT 661: Causal Inference

This course covers mathematical and statistical underpinnings of causal inference, with a focus on scientific experimentation and data-driven decision making. Topics include randomization, potential outcomes, observational studies, double robustness, semiparametric efficiency, treatment heterogeneity, policy learning, bandit algorithms, instrumental variables, regression discontinuities and graphical models. We will also discuss the relevance of optimization and machine learning tools to causal inference.
Terms: Aut | Units: 3
Instructors: Wager, S. (PI)
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