Advanced Impact Evaluation

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Course Description: 

Quantitative Policy Analysis Specialization for DPP

This course aims to expand students' understanding of causal inference by building upon the concepts covered in Impact Evaluation with R. The focus will be on econometric techniques used for causal inference with both experimental and observational data. Students will use data and the statistical package R to implement the techniques taught in class and apply them to analyze their selected policy issues. The course will cover a range of topics, including matching, regression discontinuity, difference-in-differences, event-study designs (panel data), instrumental variables, and methods for correct statistical inference.


The course runs in the second part of the Winter term, Week 7 - 12.

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.


Impact Evaluation with R (or a close substitute, where the students are already introduced to an introductory causal inference course and familiar with R).

We will use the book, "Demystifying Causal Inference: Public Policy Applications with R" for the course and selected readings.