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Implements local polynomial Regression Discontinuity (RD) point estimators with robust bias-corrected confidence intervals and inference procedures.

Project description


The rdrobust package implements the statistical inference and graphical procedures for Regression Discontinuity designs employing local polynomial and partitioning methods. It provides point estimators, confidence intervals estimators, bandwidth selectors, automatic RD plots, and many other features.

This work was supported in part by the National Science Foundation through grants SES-1357561, SES-1459931, SES-1947805 and SES-2019432.


Sebastian Calonico (

Matias D. Cattaneo (

Max H. Farrell (

Ricardo Masini (

Rocio Titiunik (


Major Upgrades

This package was first released in Spring 2014, and had two major upgrades in Fall 2016 and in Winter 2020.

  • Fall 2016 new features include: (i) major speed improvements; (ii) covariate-adjusted bandwidth selection, point estimation, and robust inference; (iii) cluster-robust bandwidth selection, point estimation, and robust inference; (iv) weighted global polynomial fits and pointwise confidence bands for RD plots; and (v) several new bandwidths selectors (e.g., different bandwidths for control and treatment groups, coverage error optimal bandwidths, and optimal bandwidths for fuzzy designs).

  • Winter 2020 new features include: (i) discrete running variable checks and adjustments; (ii) bandwidth selection adjustments for too few mass points in and/or overshooting of the support of the running variable; (iii) RD Plots with additional covariates plotted at their mean (previously the package set additional covariates at zero); (iv) automatic removal of co-linear additional covariates; (v) turn on/off standardization of variables (which may lead to small numerical/rounding discrepancies with prior versions); and (vi) rdplot output using ggplot2 in R.


To install/update use pip

pip install rdrobust


from rdrobust import rdrobust, rdbwselect, rdplot


  • numpy
  • pandas
  • scipy.linalg
  • sklearn.linear_model
  • plotnine


For overviews and introductions, see rdpackages website.

Software and Implementation

Technical and Methodological

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