Comparing inference approaches for RD designs: A reexamination of the effect of Head Start on child mortality

Resource Type: Reports & Papers
Author(s): Cattaneo, Matias D.; Titiunik, Rocio; Vazquez-Bare, Gonzalo;
Date Issued: Summer 2017
Description: The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The most common inference approaches in RD designs employ "flexible" parametric and nonparametric local polynomial methods, which rely on extrapolation and large-sample approximations of conditional expectations using observations somewhat near the cutoff that determines treatment assignment. An alternative inference approach employs the idea of local randomization, where the very few units closest to the cutoff are regarded as randomly assigned to treatment and finite-sample exact inference methods are used. In this paper, we contrast these approaches empirically by re-analyzing the influential findings of Ludwig and Miller (2007), who studied the effect of Head Start assistance on child mortality employing parametric RD methods. We first review methods based on approximations of conditional expectations, which are relatively well developed in the literature, and then present new methods based on randomization inference. In particular, we extend the local randomization framework to allow for parametric adjustments of the potential outcomes; our extended framework substantially relaxes strong assumptions in prior literature and better resembles other RD inference methods. We compare all these methods formally, focusing on both estimands and inference properties. In addition, we develop new approaches for randomization-based sensitivity analysis specifically tailored to RD designs. Applying all these methods to the Head Start data, we find that the original RD treatment effect reported in the literature is quite stable and robust, an empirical finding that enhances the credibility of the original result. All the empirical methods we discuss are readily available in general purpose software in R and Stata; we also provide the dataset and software code needed to replicate all our results. (author abstract)
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Funder(s): National Science Foundation (U.S.)
Journal Title: Journal of Policy Analysis and Management
Volume Number: 36
Issue Number: 3
Page Range: 643-681
Topics: Children & Child Development

Research & Evaluation Methods

Programs, Interventions & Curricula > Programs > Early Head Start/Head Start
Country: United States
ISSN: 0276-8739 Paper
1520-6688 Online
Peer Reviewed: yes
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