← Search

Regression Discontinuity Designs Using Covariates

Sebastian Calonico1; Matias D. Cattaneo2; Max H. Farrell3; Rocio Titiunik2

1 Columbia University · 2 Princeton University · 3 University of Chicago

The Review of Economics and Statistics 2019

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean-squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.

DOI
10.1162/rest_a_00760
Volume
101 (3)
Pages
442-451
Language
en
Export
BibTeX
Sources
openalex crossref