The Stata Journal (2014) vv, Number ii, pp. 1–36 Robust Data-Driven Inference in the Regression-Discontinuity Design

Abstract. This article introduces three Stata commands to conduct robust data-driven statistical inference in regression-discontinuity (RD) designs. First, we present rdrobust, a command that implements the robust bias-corrected confi-dence intervals proposed in Calonico, Cattaneo, and Titiunik (2014b) for average treatment effects at the cutoff in sharp RD, sharp kink RD, fuzzy RD and fuzzy kink RD designs. This command also implements other conventional nonpara-metric RD treatment effect point estimators and confidence intervals. Second, we describe the companion command rdbwselect, which implements several band-width selectors proposed in the RD literature. Finally, following the results in Calonico, Cattaneo, and Titiunik (2014d), we also introduce rdbinselect, a com-mand that implements several novel data-driven optimal choices of evenly-spaced and quantile-based bins used to approximate the underlying regression functions by local sample means, which is then used to construct the familiar RD plots usually found in empirical applications. A companion R package is described in Calonico, Cattaneo, and Titiunik (2014e)