Inference in regression discontinuity designs under local randomization
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, http://www-personal.umich.edu/∼titiunik/papers/CattaneoTitiunikVazquezBare2015_wp.pdf).
Assuming a known assignment mechanism 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.
View all articles by these authors:
Matias D. Cattaneo, Rocío Titiunik, Gonzalo Vazquez-Bare
View all articles with these 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
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