Power calculations for regression-discontinuity designs

By M. D. Cattaneo, R. Titiunik, Gonzalo Vázquez-Baré. The Stata Journal, 2019

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In this article, we introduce two commands, rdpow and rdsampsi, that conduct power calculations and survey sample selection when using local polynomial estimation and inference methods in regression-discontinuity designs. rdpow conducts power calculations using modern robust bias-corrected local polynomial inference procedures and allows for new hypothetical sample sizes and bandwidth selections, among other features. rdsampsi uses power calculations to compute the minimum sample size required to achieve a desired level of power, given estimated or user-supplied bandwidths, biases, and variances. Together, these commands are useful when devising new experiments or surveys in regression-discontinuity designs, which will later be analyzed using modern local polynomial techniques for estimation, inference, and falsification. Because our commands use the communitycontributed (and R) package rdrobust for the underlying bandwidths, biases, and variances estimation, all the options currently available in rdrobust can also be used for power calculations and sample-size selection, including preintervention covariate adjustment, clustered sampling, and many bandwidth selectors. Finally, we also provide companion R functions with the same syntax and capabilities.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/64399b0f79832fddffe5fcb7d1f9c9e752ac1653

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Citations in the corpus

A Practical Introduction to Regression Discontinuity Designs (2019, background): “See also Abadie and Cattaneo (2018) for an overview of program evaluation methods, and further 6 references on RD designs.”