2019/10/14 by Lefteris Jason Anastasopoulos, L. Jason Anastasopoulos, Anastasopoulos, L. Jason
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Applications (stat.AP) #Average treatment effect #Computer science #Covariate #Discontinuity (linguistics) #Econometrics #Econometrics (econ.EM) #Economics #Electoral Systems and Political Participation #Estimation #FOS: Computer and information sciences #FOS: Economics and business #Lasso (programming language) #Machine learning #Mathematics #Propensity score matching #Regression #Regression analysis #Regression discontinuity design #Statistical Methods and Inference #Statistics #econ.EM #stat.AP
paper · pdf · doi:10.48550/arxiv.1910.06381
First presented on August 30th, 2018 at the American Political Science Association annual conference in Boston, Massachusetts
openalex publication_date 2019/10/14 · arxiv created 2020/05/05 · arxiv updated 2020/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Regression discontinuity designs are frequently used to estimate the causal effect of election outcomes and policy interventions. In these contexts, treatment effects are typically estimated with covariates included to improve efficiency. While including covariates improves precision asymptotically, in practice, treatment effects are estimated with a small number of observations, resulting in considerable fluctuations in treatment effect magnitude and precision depending upon the covariates chosen. This practice thus incentivizes researchers to select covariates which maximize treatment effect statistical significance rather than precision. Here, I propose a principled approach for estimating RDDs which provides a means of improving precision with covariates while minimizing adverse incentives. This is accomplished by integrating the adaptive LASSO, a machine learning method, into RDD estimation using an R package developed for this purpose, adaptiveRDD. Using simulations, I show that this method significantly improves treatment effect precision, particularly when estimating treatment effects with fewer than 200 observations.