rdlocrand 1.0.4

Local Randomization Methods for RD Designs

rdlocrand: Local Randomization Methods for RD Designs

Description

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.
• rdrbounds to construct Rosenbaum bounds for sensitivity to unobserved confounders.

For more details, and related Stata and R packages useful for the analysis of RD designs, visit https://rdpackages.github.io/.

References

1. 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.
2. Cattaneo, M.D., R. Titiunik, and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.
3. 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.
4. Rosenbaum, P. (2002). Observational Studies. Springer.

Author

Matias Cattaneo, Princeton University. Email: [e-mail] Rocio Titiunik, Princeton University. Email: [e-mail] Ricardo Masini, UC Davis. Email: [e-mail] Gonzalo Vazquez-Bare, UC Santa Barbara. Email: [e-mail]