Global Testing in Multivariate Regression Discontinuity Designs

TL;DR

This paper develops a new global testing method for multivariate regression discontinuity designs that remains reliable with limited data, addressing size distortions of existing methods.

Contribution

It introduces a novel global testing procedure combining machine learning estimators with a distance-based strategy for better small-sample performance.

Findings

Maintains near-nominal size in simulations

Exhibits strong power even with limited data

Effective in empirical applications

Abstract

Regression discontinuity (RD) designs with multiple running variables arise in a growing number of empirical applications, including geographic boundaries and multi-score assignment rules. Although recent methodological work has extended estimation and inference tools to multivariate settings, far less attention has been devoted to developing global testing methods that formally assess whether a discontinuity exists anywhere along a multivariate treatment boundary. Existing approaches perform well in large samples, but can exhibit severe size distortions in moderate or small samples due to the sparsity of observations near any particular boundary point. This paper introduces a complementary global testing procedure that mitigates the small-sample weaknesses of existing multivariate RD methods by integrating multivariate machine learning estimators with a distance-based aggregation strategy, yielding a test statistic that remains reliable with limited data. Simulations demonstrate that the proposed method maintains near-nominal size and strong power, including in settings where standard multivariate estimators break down. The procedure is applied to an empirical setting to demonstrate its implementation and to illustrate how it can complement existing multivariate RD estimators.