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rd2d: Causal Inference in Boundary Discontinuity Designs
[Submitted on 12 May 2025 (v1), last revised 28 May 2026 (this v · 2026-05-29 · via stat updates on arXiv.org

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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.

Submission history

From: Matias Cattaneo [view email]
[v1] Mon, 12 May 2025 18:35:30 UTC (1,854 KB)
[v2] Tue, 10 Jun 2025 13:36:05 UTC (1,855 KB)
[v3] Thu, 28 May 2026 10:56:25 UTC (526 KB)