rd2d: Causal Inference in Boundary Discontinuity Designs

Matias D. Cattaneo; Rocio Titiunik; Ruiqi Rae Yu

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arXiv · 2505.07989

rd2d: Causal Inference in Boundary Discontinuity Designs

Matias D. Cattaneo·Rocio Titiunik·Ruiqi Rae Yu

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Abstract

Boundary Discontinuity (BD) designs are used in empirical research to learn about causal treatment effects along a continuous assignment boundary defined by a bivariate score. These designs are also known as multi-score regression discontinuity (RD) designs, and include geographic RD designs as a prominent example. This article introduces \pkg{rd2d}, a statistical software package for \proglang{R}, \proglang{Python}, and \proglang{Stata} that implements local polynomial estimation and inference for BD designs using either the bivariate score or a univariate signed distance-to-boundary score. The software covers sharp and fuzzy BD designs, providing automatic bandwidth selection, robust bias-corrected pointwise inference, uniform confidence bands, cluster-robust inference with joint or separate fitting conventions, covariate-adjusted efficiency improvements, mass-point checks, and covariance regularization, among other features. We illustrate the package with an empirical application to Opportunity Zones, where eligibility has a strong first-stage effect on designation but no significant effects on early workplace-job growth.

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Matias D. Cattaneo, Rocio Titiunik, Ruiqi Rae Yu. 2025-05-12. rd2d: Causal Inference in Boundary Discontinuity Designs. https://arxiv.org/abs/2505.07989

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