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Inference in regression discontinuity designs under local randomization

Matias Cattaneo (), Rocio Titiunik () and Gonzalo Vazquez-Bare ()
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Gonzalo Vazquez-Bare: University of Michigan, Ann Arbor

Stata Journal, 2016, vol. 16, issue 2, 331-367

Abstract: We introduce the rdlocrand package, which contains four commands to conduct finite-sample inference in regression discontinuity (RD) designs under a local randomization assumption, following the framework and methods proposed in Cattaneo, Frandsen, and Titiunik (2015, Journal of Causal Inference 3: 1–24) and Cattaneo, Titiunik, and Vazquez-Bare (2016, Working Paper, University of Michigan,∼titiunik/papers/ CattaneoTitiunikVazquezBare2015 wp.pdf). Assuming a known assignment mech- anism for units close to the RD cutoff, these functions implement a variety of procedures based on randomization inference techniques. First, the rdrandinf command uses randomization methods to conduct point estimation, hypothesis testing, and confidence interval estimation under different assumptions. Second, the rdwinselect command uses finite-sample methods to select a window near the cutoff where the assumption of randomized treatment assignment is most plausible. Third, the rdsensitivity command uses randomization techniques to conduct a sequence of hypothesis tests for different windows around the RD cutoff, which can be used to assess the sensitivity of the methods and to construct confidence intervals by inversion. Finally, the rdrbounds command implements Rosenbaum (2002, Observational Studies [Springer]) sensitivity bounds for the context of RD designs under local randomization. Companion R functions with the same syntax and capabilities are also provided. Copyright 2016 by StataCorp LP.

Keywords: rdrandinf; rdwinselect; rdsensitivity; rdrbounds; regression discontinuity designs; quasi-experimental techniques; causal inference; randomization inference; finite-sample methods; Fisher’s exact p-values; Neyman’s repeated sampling approach (search for similar items in EconPapers)
Date: 2016
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