Technology Category
Reasoning under Uncertainty: CausalitySearch and Optimization: Mixed Discrete/Continuous SearchMachine Learning: Causal Learning
rd2d: Causal Inference in Boundary Discontinuity Designs
🤖 AI Summary
This paper addresses the challenge of accurately estimating and inferring causal treatment effects under multivariate fuzzy regression discontinuity designs (RDDs) and geographic RDDs—settings where treatment assignment depends on a two-dimensional continuous boundary. We propose a novel data-driven, adaptive bandwidth selection method that supports local polynomial estimation either on the original bivariate score or on the Euclidean distance to the boundary. Within a unified bivariate nonparametric regression framework, we develop both pointwise and uniform asymptotic inference procedures. Simulations demonstrate that our approach substantially improves spatial resolution and statistical power while maintaining robustness under complex boundary curvature and heterogeneous sampling density. Our primary contribution is the first theoretically rigorous yet empirically feasible unified estimation and inference toolkit for RDDs with two-dimensional boundaries.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Implements causal inference for boundary discontinuity designs
Extends methods for Multi-Score Regression Discontinuity designs
Provides data-driven bandwidth selection for local estimation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses local polynomial estimation for boundary discontinuity
Implements data-driven bandwidth selection procedures
Provides pointwise and uniform estimation along boundary
🔎 Similar Papers
No similar papers found.
