“On Estimating Multiple Treatment Effects with Regression,” Paul Goldsmith-Pinkham, Yale SOM

QUANTITATIVE RESEARCH METHODS WORKSHOP

Abstract: We study the causal interpretation of regressions on multiple dependent treatments and flexible controls. Such regressions are often used to analyze randomized control trials with multiple intervention arms, and to estimate institutional quality (e.g. teacher value-added) with observational data. We show that, unlike with a single binary treatment, these regressions do not generally estimate convex averages of causal effects-even when the treatments are conditionally randomly assigned and the controls fully address omitted variables bias. We discuss different solutions to this issue, and propose as a solution a new class of efficient estimators of weighted average treatment effects.

Paul Goldsmith-Pinkham’s research interests include consumer & corporate finance, econometrics, and social networks. His current work focuses on assessing the costs and benefits of debtor protection policies and understanding the role that consumer debt plays in the macroeconomy. Paul’s research also studies machine learning techniques applied to economics questions. Before joining Yale, Paul was a Research Economist at the Federal Reserve Bank of New York. He earned a bachelor’s degree in economics from the Swarthmore College, and a PhD in economics from the Harvard University.

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The series is sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.