Advanced Impact Evaluation: Management and Methodology

Course Description: 

Elective Course

The course focuses on statistical inference for strategic decision-making across a wide range of contexts, especially with respect to Big Data. Technical aspects of the course will focus on computational approaches and real-world challenges. At times we will be joined by project managers from NGOs and government agencies based in Budapest who will present real-world inferential challenges and ask us for help designing solutions by applying concepts from this course. The course will also reinforce the importance effectively articulating findings, methodology, and policy/decision-relevant recommendations to different audiences.

Learning Outcomes: 

Students will learn to model cause-and-effect relationships and develop counterfactual scenarios. They will gain experience using computational methods to predict the impacts of policies, interventions, and events, while learning to avoid common pitfalls. By the end, students should be able to: (1) think through which of the methods covered in class (if any) would be best suited to solve a given decision problem and what data would be required; (2) perform appropriate analysis and produce results; (3) connect those results to strategic decision-making; (4) critically examine statistical causal claims put forward by others; and (5) present findings and recommendations effectively for audiences of varying sophistication.

Assessment: 

Homework assignments, a midterm, a final exam, and short quizzes on the required reading. 

Prerequisites: 

First priority: students taking the Impact Evaluation: Theory and Application course