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Matias D. Cattaneo

Princeton University, US, and Amazon Inc., US

I am delighted to contribute an overview piece on recent methodology for regression discontinuity (RD) designs. I hope that policymakers and academics alike will find World of Labor a useful way to quickly learn the latest RD methodological research, and how to use it in real-world applications.

IZA World of Labor role

Author

Current position

Professor, Princeton University, US

Research interest

Econometrics, program evaluation

Website

https://cattaneo.princeton.edu/

Positions/functions as a policy advisor

Amazon Schoolar (consultant position)

Past positions

Professor, Michigan University

Qualifications

Ph.D. in Economics, UC Berkeley, 2008.

Selected publications

  • "On Binscatter." American Economic Review 114:5 (2024): 1488–1514 (with R. K. Crump, M. H. Farrell, and Y. Feng).

  • "Simple Local Polynomial Density Estimators." Journal of the American Statistical Association 115: 531 (2019): 1449–1455 (with D. Jansson and X. Ma).

  • "Two-Step Estimation and Inference with Possibly Many Included Covariates Get access Arrow." Review of Economic Studies 86:3 (2019): 1095–1122 (with M. Jansson and X. Ma)

  • "Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency." Econometrica 86:3 (2018): 955-995 (with M. Jansson).

  • "Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs." Econometrica 82:6 (2014): 2295-2326 (with S. Calonico and R. Titiunik).

article(s)

  • Leveraging covariates in regression discontinuity designs

    Proper use of covariates in regression discontinuity designs can enhance empirical scientific discoveries and evidence-based policy decisions

    Matias D. CattaneoFilippo Palomba, November 2025
    It is common practice to incorporate additional covariates in empirical economics. In the context of regression discontinuity (RD) designs, covariate adjustment plays multiple roles, making it essential to understand its impact on analysis and conclusions. Typically implemented via local least squares regressions, covariate adjustment can serve three main distinct purposes: (i) improving the efficiency of RD average causal effect estimators, (ii) learning about heterogeneous RD policy effects, and (iii) changing the RD parameter of interest.
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