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This course is intended as Ph.D.-level introduction to applied statistics, with a focus on the identification of causal effects in the tradition of the Neyman/Rubin potential outcomes framework. Prior coursework in applied statistics or quantitative methods will be useful but is not required. Upon completion of the course, students will be in a position to understand and critically assess scholarship that uses instrumental variables, difference-in-differences, regression discontinuity, and other quasi- and natural-experimental research designs. Formal mathematical proof will be kept to a minimum. Students will be asked to adapt existing code and write some of their own code in R.