Generic Covariate Adjustment for Regression Discontinuity Designs

2026-09-24

AI Summary

This study addresses the lack of theoretical justification for covariate adjustment in regression discontinuity designs and the inconsistency of nonlinear estimators, such as quantile treatment effects. To overcome these limitations, this work proposes a general covariate adjustment framework based on entropy balancing reweighting. By transcending conventional linear restrictions, the proposed approach achieves consistent and efficient nonlinear estimation. Simulation studies and empirical applications validate the theoretical findings, demonstrating that the method substantially improves estimation efficiency. Notably, it yields statistically significant results in empirical settings that remain undetected without covariate adjustment. Ultimately, this research provides a robust analytical tool for causal inference.

Abstract

It is standard practice to include covariates in regression discontinuity designs (RDDs) and regression kink designs (RKDs), but the theoretical justification for doing so does not generally extend beyond linear estimands. This paper proposes a novel entropy balancing reweighting approach for covariate adjustment within a general framework of RDDs and RKDs. While conventional regression-based covariate adjustment methods generally fail to deliver consistent estimation for nonlinear estimands such as quantile treatment effects, our reweighting approach achieves consistency while improving efficiency. Moreover, even in settings where the regression-based covariate adjustment method already improves efficiency, our approach can deliver additional efficiency gains. Simulation studies corroborate these theoretical findings. We present an empirical application in which our covariate adjustment yields statistically significant results that would not be obtained without covariate adjustment.

Problem

• Regression Discontinuity Design
• Covariate Adjustment
• Nonlinear Estimands
• Consistency
• Efficiency

Innovation

• Entropy Balancing
• Regression Discontinuity Design
• Covariate Adjustment
• Reweighting
• Nonlinear Estimands