Advanced Impact Evaluation

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Course Description: 

Quantitative Policy Analysis Specialization for DPP

This class will build on Impact Evaluation I to broaden student knowledge regarding impact evaluation, focusing on econometric techniques used for causal inference with experimental and observational data. Students will find and construct data sets to answer current policy questions and use the statistical package R to implement the techniques learned in class and apply these tools to analyze their chosen policy. Topics include matching, regression discontinuity, difference-in-difference and event-study designs (panel data), instrumental variables, and techniques for correct statistical inference.


Learning Outcomes: 

By the end of the course, students will be able to think critically about causal inference in the context of policy evaluation. Students should be able to select the appropriate econometric technique(s) to evaluate policies in an array of contexts and identify potential issues. In addition, students should be able to use R to manage data and conduct econometric analysis adeptly. Students should also be able to produce a research report that can be used to inform policy-makers.