This is an intensive methodological and applied course aimed at developing useful skills in impact evaluation methods and practice, focused on 2 key aspects: (a) statistical causal inference, and (b) evaluation management across the evaluation lifecycle, from pre-implementation to post-completion. Statistical content will include randomized control trials, matching-based methods, and the synthetic control method; students will learn how to use these tools for analyzing and estimating the causal effects of public policy interventions, private sector initiatives, and other events and activities. After completing this class, students will understand the role of counterfactuals in impact evaluation and should be able to use statistical software (the "R" programming language) to estimate impacts. Homework will utilize real data drawn from real cases. No prior statistical coursework or programming experience is required.
- Teacher: Alexis Diamond