Regression Discontinuity Designs Using Covariates

Sebastian Calonico, Matias Cattaneo, Max Farrell and Rocio Titiunik

The Review of Economics and Statistics, 2019, vol. 101, issue 3, 442-451

Abstract: 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.

Date: 2019
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Working Paper: Regression Discontinuity Designs Using Covariates (2018)
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