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STATS 361: Causal Inference - Stanford University

STATS 361: Causal InferenceStefan WagerStanford UniversitySpring 2020 Contents1 Randomized Controlled Trials22 Unconfoundedness and the Propensity Score93 Efficient Treatment Effect Estimation via Augmented IPW184 Estimating Treatment Heterogeneity275 Regression Discontinuity Designs356 Finite Sample Inference in RDDs437 Balancing Estimators528 Methods for Panel Data619 Instrumental Variables Regression6810 Local Average Treatment Effects7411 Policy Learning8312 Evaluating Dynamic Policies9113 Structural Equation Modeling9914 Adaptive Experiments1071 Lecture 1 Randomized Controlled TrialsRandomized controlled trials (RCTs) form the foundation of statistical causalinference.

causal e ect of the treatment on the i-th unit is then1 i= Y i(1) Y i(0): (1.1) The fundamental problem in causal inference is that only one treatment can be assigned to a given individual, and so only one of Y i(0) and Y i(1) can ever be observed. Thus, i can never be observed.

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