The regression discontinuity (RD) design is a popular quasi-experimental design
for causal inference and policy evaluation. Under the local randomization approach,
RD designs can be interpreted as randomized experiments inside a window around the
rdlocrand package provides tools to analyze RD designs under local
rdrandinf to perform hypothesis
testing using randomization inference,
rdwinselect to select a window
around the cutoff in which randomization is likely to hold,
to assess the sensitivity of the results to different window lengths and null hypotheses
rdrbounds to construct Rosenbaum bounds for sensitivity to
unobserved confounders. For more details, and related
useful for analysis of RD designs, visit https://rdpackages.github.io/.
Matias Cattaneo, Princeton University. firstname.lastname@example.org
Rocio Titiunik, Princeton University. email@example.com
Gonzalo Vazquez-Bare, UC Santa Barbara. firstname.lastname@example.org
Cattaneo, M.D., B. Frandsen and R. Titiunik. (2015). Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate. Journal of Causal Inference 3(1): 1-24.
Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.
Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2017). Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality. Journal of Policy Analysis and Management 36(3): 643-681.
Rosenbaum, P. (2002). Observational Studies. Springer.
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