2025/03/12 by Aditya Ghosh, Guido Imbens, Guido W. Imbens +4 · 2 voices · 2 citations
Decision Sciences · Mathematics · #Advanced Causal Inference Techniques #Forecasting Techniques and Applications #Statistical Methods and Inference #econ.EM #stat.ME
paper · pdf · doi:10.48550/arxiv.2503.09907
openalex publication_date 2025/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that widely used approaches to building confidence intervals in regression discontinuity designs exhibit suboptimal behavior in practice: In a simulation study calibrated to high-profile applications of regression discontinuity designs, existing methods either have systematic under-coverage or have wider-than-necessary intervals. We propose a new approach, partially linear regression discontinuity inference (PLRD), and find it to address shortcomings of existing methods: Throughout our experiments, confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD under smoothness assumptions.