2017/08/17 by Andrew Gelman, Guido Imbens, Guido W. Imbens · 12 citations
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · doi:10.1080/07350015.2017.1366909
openalex publication_date 2017/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.