Flexible Covariate Adjustments in Regression Discontinuity Designs

Category: Quantitative Economic Policy Seminar
When: 10 November 2022
, 14:00
 - 15:15
Where: HoF 1.28 / Shanghai


Empirical regression discontinuity (RD) studies often use covariates to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than the linear adjustment estimators that are currently  used widely in practice. Our approach can accommodate a possibly large number of either discrete or continuous covariates. It involves running a standard RD analysis with an appropriately modified outcome variable, which takes the form of the difference between the original outcome and a function of the  covariates. We characterize the function that leads to the estimator with the smallest asymptotic variance, and show how it can be estimated via modern machine learning, nonparametric regression, or classical parametric methods. The resulting estimator is easy to implement because tuning parameters can be chosen as in a conventional RD analysis. An extensive simulation study illustrates the performance of our approach.