text-only page produced automatically by Usablenet Assistive Skip all navigation and go to page content Skip top navigation and go to directorate navigation Skip top navigation and go to page navigation
National Science Foundation
design element
Search Awards
Recent Awards
Presidential and Honorary Awards
About Awards
Grant Policy Manual
Grant General Conditions
Cooperative Agreement Conditions
Special Conditions
Federal Demonstration Partnership
Policy Office Website

Award Abstract #1357561

New Methodological Developments for Inference in the Regression-Discontinuity Design

Divn Of Social and Economic Sciences
divider line
Initial Amendment Date: April 11, 2014
divider line
Latest Amendment Date: April 11, 2014
divider line
Award Number: 1357561
divider line
Award Instrument: Standard Grant
divider line
Program Manager: Nancy A. Lutz
SES Divn Of Social and Economic Sciences
SBE Direct For Social, Behav & Economic Scie
divider line
Start Date: July 1, 2014
divider line
End Date: June 30, 2017 (Estimated)
divider line
Awarded Amount to Date: $276,756.00
divider line
Investigator(s): Matias Cattaneo cattaneo@umich.edu (Principal Investigator)
Rocio Titiunik (Co-Principal Investigator)
divider line
Sponsor: University of Michigan Ann Arbor
3003 South State St. Room 1062
Ann Arbor, MI 48109-1274 (734)763-6438
divider line
divider line
Program Reference Code(s):
divider line
Program Element Code(s): 1333, 1320


This award funds the development of new methods for the statistical analysis of social science data. The project focuses on 'regression discontinuity' (RD) models, which are very widely used in practice. This kind of statistical model is used to estimate the effect of a treatment on an outcome when unobserved factors may threaten the validity of the statistical analysis. While RD models are widely used to analyze data, many important methodological and statistical features of the models are not well understood. This project develops novel methods for the analysis of RD models and applies the results to concrete empirical problems in Economics and other social sciences.

The research develops new methodological and practical tools for estimation, inference and falsification of RD designs. This includes a method that accounts for the fact that RD estimators are sensitive to the choice of bandwidth, a method that is valid in finite samples, the analysis of RDs with multiple cutoffs and the development of new tools for spatial RD designs.

Broader impacts of this research will come from the use of these new methods to provide better insight for economic policy.


Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Luke Keele, Rocio Titiunik. "Geographic Boundaries as Regression Discontinuities," Political Analysis, v.23, 2015, p. 127.

Matias D. Cattaneo, Brigham Frandsen, Rocio Titiunik. "Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate," Journal of Causal Inference, v.3, 2015, p. 1.

Rocio Titiunik. "Can Big Data Solve the Fundamental Problem of Causal Inference?," PS: Political Science & Politics, v.48, 2015, p. 74.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "Robust Data-Driven Inference in the Regression-Discontinuity Design," Stata Journal, v.14, 2014, p. 909.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs," Econometrica, v.82, 2014, p. 2295.

Luke Keele, Rocio Titiunik. "Natural Experiments Based on Geography," Political Science Research and Methods, v.4, 2016, p. 65.

Robert Erikson, Rocio Titiunik. "Using Regression Discontinuity to Uncover the Personal Incumbency Advantage," Quarterly Journal of Political Science, v.10, 2015, p. 101.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "Optimal Data-Driven Regression Discontinuity Plots," Journal of the American Statistical Association, v.110, 2015, p. 1753.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs," R Journal, v.7, 2015, p. 38.


Please report errors in award information by writing to: awardsearch@nsf.gov.



Print this page
Back to Top of page
Research.gov  |  USA.gov  |  National Science Board  |  Recovery Act  |  Budget and Performance  |  Annual Financial Report
Web Policies and Important Links  |  Privacy  |  FOIA  |  NO FEAR Act  |  Inspector General  |  Webmaster Contact  |  Site Map
National Science Foundation Logo
The National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230, USA
Tel: (703) 292-5111, FIRS: (800) 877-8339 | TDD: (800) 281-8749
  Text Only Version