rdlocrand 2.0

Local Randomization Methods for RD Designs

Local Randomization Methods for RD Designs

Description

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

Source code is available at https://github.com/rdpackages/rdlocrand.

Authors

Matias D. Cattaneo, Princeton University. Email: [e-mail]

Ricardo Masini, UC Davis. Email: [e-mail]

Rocio Titiunik, Princeton University. Email: [e-mail]

Gonzalo Vazquez-Bare, UC Santa Barbara. Email: [e-mail]

Installation

To install or update from PyPI:

pip install rdlocrand

Usage

from rdlocrand import rdrandinf, rdwinselect, rdsensitivity, rdrbounds

out = rdrandinf(Y, R, wl=-0.75, wr=0.75, quietly=True)
print(out["p.value"])

Package functions return dictionaries whose keys match the names documented in the function docstrings, such as p.value, obs.stat, results, and p.values.

References

For overviews and introductions, see rdpackages website.

• 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.