RDPOWER

The package rdpower implements power, sample size, and minimum detectable effect calculations for Regression Discontinuity (RD) designs using local polynomial methods.

• rdpower: ex-post power calculations for RD treatment effects.
• rdsampsi: required sample size calculations for target power.
• rdmde: minimum detectable effect calculations.

See references for methodological and practical details.

Website: https://rdpackages.github.io/.

Source code: https://github.com/rdpackages/rdpower.

Authors

Matias D. Cattaneo (matias.d.cattaneo@gmail.com)

Ricardo Masini (ricardo.masini@gmail.com)

Rocio Titiunik (rocio.titiunik@gmail.com)

Gonzalo Vazquez-Bare (gvazquezbare@gmail.com)

Installation

To install/update use pip:

pip install rdpower

Usage

import pandas as pd

from rdpower import rdmde, rdpower, rdsampsi

data = pd.read_csv("rdpower_senate.csv")
z = data[["demvoteshfor2", "demmv"]]

power = rdpower(data=z, tau=5)
sample_size = rdsampsi(data=z, tau=5)
mde = rdmde(data=z)

• Replication: rdpower illustration, senate data.

Dependencies

• numpy
• pandas
• scipy
• rdrobust
• matplotlib

References

For overviews and introductions, see rdpackages website.

Software and Implementation

• Cattaneo, Titiunik and Vazquez-Bare (2019): Power Calculations for Regression Discontinuity Designs.
  Stata Journal 19(1): 210-245.

Technical and Methodological

• Calonico, Cattaneo and Titiunik (2014): Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.
  Econometrica 82(6): 2295-2326.
  Supplemental Appendix.

• Calonico, Cattaneo, Farrell and Titiunik (2019): Regression Discontinuity Designs Using Covariates.
  Review of Economics and Statistics 101(3): 442-451.
  Supplemental Appendix.

• Calonico, Cattaneo and Farrell (2020): Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs.
  Econometrics Journal 23(2): 192-210.
  Supplemental Appendix.