rd2d: Causal Inference in Boundary Discontinuity Designs

TL;DR

The paper introduces the R package rd2d for causal inference in boundary discontinuity designs, enabling local polynomial estimation with novel bandwidth selection and inference methods, demonstrated through simulations.

Contribution

It provides a comprehensive software implementation extending existing methods for boundary discontinuity designs with new data-driven bandwidth selection and inference features.

Findings

Effective in simulation studies

Improves estimation accuracy near boundaries

Supports both pointwise and uniform inference

Abstract

Boundary discontinuity designs -- also known as Multi-Score Regression Discontinuity (RD) designs, with Geographic RD designs as a prominent example -- are often used in empirical research to learn about causal treatment effects along a continuous assignment boundary defined by a bivariate score. This article introduces the R package rd2d, which implements and extends the methodological results developed in Cattaneo, Titiunik and Yu (2025) for boundary discontinuity designs. The package employs local polynomial estimation and inference using either the bivariate score or a univariate distance-to-boundary metric. It features novel data-driven bandwidth selection procedures, and offers both pointwise and uniform estimation and inference along the assignment boundary. The numerical performance of the package is demonstrated through a simulation study.