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This course introduces strategies for estimating causal effects from a counterfactual perspective, uniting the potential outcome model with causal graph methodology. After an examination of the primary features of the counterfactual perspective and criteria for causal effect identification, the course focuses on developing a deep understanding of data analysis techniques that can work in favorable circumstances, such as matching, regression from a potential outcome perspective, and inverse probability of treatment weighting. The course concludes with the vexing challenges posed by unobserved determinants of both the cause and outcome of interest, and it provides a review of specialized designs that can salvage a research project in these situations.