PLRD: Partially Linear Regression Discontinuity Inference

Aditya Ghosh, Guido Imbens and Stefan Wager

Papers from arXiv.org

Abstract: Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that the widely used approaches to building confidence intervals in regression discontinuity designs often exhibit suboptimal behavior in practice. We propose a new estimator, the partially linear regression discontinuity (PLRD) estimator that, in set of a simulation studies carefully calibrated to twelve high-profile applications of regression discontinuity designs, has substantially lower estimation error than available comparison methods. Throughout our experiments, the confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD. Our simulation study serves as a general template for how new econometric methods can be credibly evaluated relative to the existing alternatives by constructing simulation designs that generate synthetic data indistinguishable from the original data using the Wasserstein generative adversarial network methodology.

Date: 2025-03, Revised 2026-08
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