Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik

Research output: Contribution to journalArticlepeer-review

1276 Scopus citations


In the regression-discontinuity (RD) design, units are assigned to treatment based on whether their value of an observed covariate exceeds a known cutoff. In this design, local polynomial estimators are now routinely employed to construct confidence intervals for treatment effects. The performance of these confidence intervals in applications, however, may be seriously hampered by their sensitivity to the specific bandwidth employed. Available bandwidth selectors typically yield a "large" bandwidth, leading to data-driven confidence intervals that may be biased, with empirical coverage well below their nominal target. We propose new theory-based, more robust confidence interval estimators for average treatment effects at the cutoff in sharp RD, sharp kink RD, fuzzy RD, and fuzzy kink RD designs. Our proposed confidence intervals are constructed using a bias-corrected RD estimator together with a novel standard error estimator. For practical implementation, we discuss mean squared error optimal bandwidths, which are by construction not valid for conventional confidence intervals but are valid with our robust approach, and consistent standard error estimators based on our new variance formulas. In a special case of practical interest, our procedure amounts to running a quadratic instead of a linear local regression. More generally, our results give a formal justification to simple inference procedures based on increasing the order of the local polynomial estimator employed. We find in a simulation study that our confidence intervals exhibit close-to-correct empirical coverage and good empirical interval length on average, remarkably improving upon the alternatives available in the literature. All results are readily available in R and STATA using our companion software packages described in Calonico, Cattaneo, and Titiunik (2014d, 2014b).

Original languageEnglish (US)
Pages (from-to)2295-2326
Number of pages32
Issue number6
StatePublished - Nov 1 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


  • Alternative asymptotics
  • Bias correction
  • Local polynomials
  • Regression discontinuity
  • Robust inference


Dive into the research topics of 'Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs'. Together they form a unique fingerprint.

Cite this