Workshop description:

This three-day workshop covers a wide range of practical results for regression and IV-based analyses of causal effects which leverage random or conditionally as-good-as-random shocks. Questions of particular focus include:

• "What controls do I need to include to avoid omitted variables bias?"
• "Do I need to worry about 'negative weighting' of heterogeneous effects?"
• "How should I be clustering my standard errors?"
• "What's the payoff to considering nonlinear/'structural' analyses?"

Results will be illustrated through several real-world applications.

This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of instrumental variables and will cover the frontiers of the topic.and will cover advanced methods. A solid understanding of the material covered in the material from Scott's courses (Part 1 and Part 2) will be assumed.

All course material is available free and open source via our Github Repository.

Daily Structure:

This is a 3-day workshop. The goal of the workshop is for students to gain enough knowledge from the lectures and experience from the programming activities that they become confident and capable enough to implement and interpret these methods in their own work, as well as continue to learn this new material on their own after the workshop concludes. Each day lasts 3 hours with lectures and "coding together” sessions. After each day, a coding lab will be assigned to be completed between days and then reviewed between days.

About the instructor:

Peter Hull is the Groos Family Assistant Professor of Economics at Brown Univeristy and a Faculty Research Fellow at the National Bureau of Economic Research. He has published papers on topics in applied econometrics, education, healthcare, and criminal justice, in outlets such as the American Economic Review, the Quarterly Journal of Economics, the Review of Economic Studies, and the New England Journal of Medicine. His research is focused on developing and applying new instrumental variable methods to measure the quality of institutions, such as schools or hospitals, as well as discrimination and bias in human and algorithmic decision-making. Prior to Brown, Professor Hull taught at the Kenneth C. Griffin Department of Economics at the University of Chicago and worked at Microsoft Research and the Federal Reserve Bank of New York. He earned his PhD in economics from MIT in 2017, under 2021 Nobel Laureate Josh Angrist.

International and Student Pricing:

Email [email protected] for student and international pricing.