Flexible Covariate Adjustments in Regression Discontinuity Designs

Claudia Noack, Tomasz Olma and Christoph Rothe

Papers from arXiv.org

Abstract: 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 existing methods and can accommodate many covariates. It involves running a standard RD analysis in which a function of the covariates has been subtracted from the original outcome variable. We characterize the function that leads to the estimator with the smallest asymptotic variance, and consider feasible versions of such estimators in which this function is estimated, for example, through modern machine learning techniques.

Date: 2021-07, Revised 2024-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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