https://github.com/rdpackages/rdpower
Power and sample size calculations for RD designs using robust bias-corrected local polynomial inference.
https://github.com/rdpackages/rdpower
causal-inference program-evaluation regression-discontinuity-designs treatment-effects
Last synced: about 2 months ago
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Power and sample size calculations for RD designs using robust bias-corrected local polynomial inference.
- Host: GitHub
- URL: https://github.com/rdpackages/rdpower
- Owner: rdpackages
- License: other
- Created: 2020-08-07T12:39:20.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-05-23T03:54:50.000Z (9 months ago)
- Last Synced: 2025-09-25T10:43:41.889Z (5 months ago)
- Topics: causal-inference, program-evaluation, regression-discontinuity-designs, treatment-effects
- Language: Stata
- Homepage:
- Size: 3.91 MB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RDPOWER
The ``rdpower`` package provides Python, R, and Stata implementations of power, sample size, and minimum detectable effects calculations using robust bias-corrected local polynomial inference methods.
This work was supported by the National Science Foundation through grant [SES-1357561](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357561).
## Website
https://rdpackages.github.io/rdpower
## Queries and Requests
Please email: [rdpackages@googlegroups.com](mailto:rdpackages@googlegroups.com)
## Major Upgrades
This package was first released in Fall 2016, and had one major upgrade in Fall 2020.
- _Fall 2020 new feature_: command/function `rdmde` for computing minimum detectable effects.
## Python Implementation
To install/update in Python type:
```
pip install rdpower
```
- Help: [PYPI repository](https://pypi.org/project/rdpower/).
- Replication: [py-script](python/rdpower_illustration.py), [senate data](python/rdpower_senate.csv).
## R Implementation
To install/update in R type:
```
install.packages('rdpower')
```
- Help: [R Manual](https://cran.r-project.org/web/packages/rdpower/rdpower.pdf), [CRAN repository](https://cran.r-project.org/package=rdpower).
- Replication files: [R-script](R/rdpower_illustration.R), [data-senate](R/rdpower_senate.csv).
## Stata Implementation
To install/update in Stata type:
```
net install rdpower, from(https://raw.githubusercontent.com/rdpackages/rdpower/master/stata) replace
```
- Help: [rdpower](stata/rdpower.pdf), [rdsampsi](stata/rdsampsi.pdf), [rdmde](stata/rdmde.pdf).
- Replication: [do-file](stata/rdpower_illustration.do), [data-senate](stata/rdpower_senate.dta).
## References
For overviews and introductions, see [rdpackages website](https://rdpackages.github.io).
### Software and Implementation
- Cattaneo, Titiunik and Vazquez-Bare (2019): [Power Calculations for Regression Discontinuity Designs](https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf).
_Stata Journal_ 19(1): 210-245.
### Technical and Methodological
- Calonico, Cattaneo and Titiunik (2014): [Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs](https://rdpackages.github.io/references/Calonico-Cattaneo-Titiunik_2014_ECMA.pdf).
_Econometrica_ 82(6): 2295-2326.
[Supplemental Appendix](https://rdpackages.github.io/references/Calonico-Cattaneo-Titiunik_2014_ECMA--Supplemental.pdf).
- Calonico, Cattaneo, Farrell and Titiunik (2019): [Regression Discontinuity Designs Using Covariates](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell-Titiunik_2019_RESTAT.pdf).
_Review of Economics and Statistics_ 101(3): 442-451.
[Supplemental Appendix](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell-Titiunik_2019_RESTAT--Supplement.pdf).
- Calonico, Cattaneo and Farrell (2020): [Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell_2020_ECTJ.pdf).
_Econometrics Journal_ 23(2): 192-210.
[Supplemental Appendix](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell_2020_ECTJ--Supplement.pdf).