rddapp-package: Regression Discontinuity Design Application

rddapp-packageR Documentation

Regression Discontinuity Design Application


rddapp: A package for regression discontinuity designs (RDDs).


The rddapp package provides a set of functions for the analysis of the regression-discontinuity design (RDD). The three main parts are: estimation of effects of interest, power analysis, and assumption checks.


A variety of designs can be estimated in various ways. The single-assignment RDD (both sharp and fuzzy) can be analyzed using both a parametric (global) or non-parametric (local) approach. The multiple-assignment RDD (both sharp and fuzzy) can be analyzed using both parametric and non-parametric estimation. The analysis choices are further to use estimate effects based on univariate scaling, the centering approach, or the frontier approach. The frontier approach can currently only be estimated using parametric regression with bootstrapped standard errors.

Power analysis

Statistical power can be be estimated for both the single- and multiple-assignment RDD, (both sharp and fuzzy), including all parametric and non-parametric estimators mentioned in the estimation section. All power analyses are based on a simulation approach, which means that the user has to provide all necessary parameters for a data-generating model.

Assumption checks

An important part of any RDD are checks of underlying assumptions. The package provides users with the option to estimate McCrary's sorting test (to identify violations of assignment rules), checks of discontinuities of other baseline covariates, along with sensitivity checks of the chosen bandwidth parameter for non-parametric models, and so-called placebo tests, that examine the treatment effect at other cut-points along the assignment variable.


Ze Jin zj58@cornell.edu, Wang Liao wl483@cornell.edu, Irena Papst ip98@cornell.edu, Wenyu Zhang wz258@cornell.edu, Kimberly Hochstedler kah343@cornell.edu, Felix Thoemmes, fjt36@cornell.edu

rddapp documentation built on April 6, 2023, 1:15 a.m.