Flexible covariate adjustments in regression discontinuity designs
4 affiliations
- 1Department of Economics, University of Bonn, Germany
- 2University of Mannheim*
- 3Department of Statistics, Ludwig Maximilian University of Munich, Germany
- 4Department of Economics, University of Mannheim, Germany
Abstract
Empirical regression discontinuity (RD) studies often include covariates in their specifications 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. Our estimators are simple to implement and involve running a standard RD analysis after subtracting a function of the covariates from the original outcome variable. We characterize the function of the covariates that minimizes the asymptotic variance of these estimators. We also show that the conventional RD framework gives rise to a special robustness property which implies that the optimal adjustment function can be estimated flexibly via modern machine learning techniques without affecting the first-order properties of the final RD estimator. We demonstrate our methods’ scope for efficiency improvements by reanalyzing data from a large number of recently published empirical studies.
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BibTeX
@article{noack2026flexible,
title = {{Flexible covariate adjustments in regression discontinuity designs}},
author = {Claudia Noack and Tomasz Olma and Christoph Rothe},
journal = {Journal of Econometrics},
year = {2026},
month = jul,
doi = {10.1016/j.jeconom.2026.106298},
url = {https://doi.org/10.1016/j.jeconom.2026.106298},
note = {CC BY},
}
RIS
TY - JOUR
TI - Flexible covariate adjustments in regression discontinuity designs
AU - Claudia Noack
AU - Tomasz Olma
AU - Christoph Rothe
PY - 2026
DA - 2026/07/21
JO - Journal of Econometrics
DO - 10.1016/j.jeconom.2026.106298
UR - https://doi.org/10.1016/j.jeconom.2026.106298
AB - Empirical regression discontinuity (RD) studies often include covariates in their specifications 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. Our estimators are simple to implement and involve running a standard RD analysis after subtracting a function of the covariates from the original outcome variable. We characterize the function of the covariates that minimizes the asymptotic variance of these estimators. We also show that the conventional RD framework gives rise to a special robustness property which implies that the optimal adjustment function can be estimated flexibly via modern machine learning techniques without affecting the first-order properties of the final RD estimator. We demonstrate our methods’ scope for efficiency improvements by reanalyzing data from a large number of recently published empirical studies.
SN - 0304-4076
SN - 1872-6895
ER -