rdlocrand-package: rdlocrand: Local Randomization Methods for RD Designs

rdlocrand-packageR Documentation

rdlocrand: Local Randomization Methods for RD Designs


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 cutoff. The rdlocrand package provides tools to analyze RD designs under local randomization: rdrandinf to perform hypothesis testing using randomization inference, rdwinselect to select a window around the cutoff in which randomization is likely to hold, rdsensitivity to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds to construct Rosenbaum bounds for sensitivity to unobserved confounders. For more details, and related Stata and R packages useful for analysis of RD designs, visit https://rdpackages.github.io/.


Matias Cattaneo, Princeton University. cattaneo@princeton.edu

Rocio Titiunik, Princeton University. titiunik@princeton.edu

Gonzalo Vazquez-Bare, UC Santa Barbara. gvazquez@econ.ucsb.edu


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.

rdlocrand documentation built on June 22, 2022, 1:05 a.m.