Local Randomization Methods for RD Designs
Project description
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
- 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.
Author
Matias Cattaneo, Princeton University. Email: cattaneo@princeton.edu Rocio Titiunik, Princeton University. Email: titiunik@princeton.edu Ricardo Masini, UC Davis. Email: rmasini@ucdavis.edu Gonzalo Vazquez-Bare, UC Santa Barbara. Email: gvazquez@econ.ucsb.edu
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for rdlocrand-1.0.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 53708970f71f06f0b856cd7c89366063af03cce3521eec118432bc373a7ead07 |
|
MD5 | 06f6b95a5c46c7a3e205c6e8a159c1b7 |
|
BLAKE2b-256 | 81638d2dc7d8d45cf195e808c30326b0ac8b4b7852872adcfbd6eb92290bd11d |