3.0

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Statistical underpinnings of causal inference, with a focus on experimental design and data-driven decision making, as well as the incorporation of tools from optimization and machine learning. Topics include randomization and the potential outcomes framework, confounding adjustment via propensity scores and matching, double robustness and semiparametric efficiency, treatment heterogeneity, instrumental variables, regression discontinuities, synthetic controls, sensitivity analysis, interference, graphical models, and policy learning.