Artem Samiahulin

Global Testing in Multivariate Regression Discontinuity Designs

Independent work

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.

Iterated Random Forest Feasible Generalized Least Squares

with Drew Creal, Marcelo Medeiros, and Rodrigo Sarlo

Understanding the Impact of Evidence-Based Funding on Educational Outcomes: Evidence from Illinois School Districts

Independent work

Optimal Distance for Regression Discontinuity Designs

Independent work

State Dependence and Food Stamps: Before and After the Personal Responsibility and Work Opportunity Reconciliation Act of 1996

27th Annual Issues of Political Economy Conference — February 2020