Matias D. Cattaneo

About

Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering at Princeton University and an Amazon Scholar. He studies the mathematical foundations of data science at the intersection of econometrics, statistics, machine learning, and artificial intelligence. His research develops statistical and computational methods for the social, behavioral, and biomedical sciences, with emphasis on program evaluation and causal inference. Matias was awarded a 2026 Guggenheim Fellowship in Data Science. He is an elected Member of the International Statistical Institute and an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Association for Applied Econometrics. His research has also been recognized through multiple paper awards, journal distinctions, invited lectures, and highly cited publications. Matias earned a Ph.D. in Economics and an M.A. in Statistics from the University of California, Berkeley, a Master in Economics from Universidad Torcuato Di Tella, and a Licentiate in Economics from Universidad de Buenos Aires. Originally from Buenos Aires, Argentina, he is married to Rocio Titiunik, and they have two daughters, Lucero (Lulu) and Maite.

Research topics

• Political Science
• Statistics
• Applied mathematics
• Mathematics

Selected publications

• On Binscatter

  American Economic Review · 2024 · 115 citations

  1st authorCorresponding

  Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing. We study the properties of this method formally and develop enhanced visualization and econometric binscatter tools. These include estimating conditional means with optimal binning and quantifying uncertainty. We also highlight a methodological problem related to covariate adjustment that can yield incorrect conclusions. We revisit two applications using our methodology and find…

  PublisherDOI

• Large sample properties of partitioning-based series estimators

  The Annals of Statistics · 2020 · 45 citations

  1st authorCorresponding

  We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, econometrics and machine learning. First, we obtain a general characterization of their leading asymptotic bias. Second, we establish integrated mean squared error approximations for the point estimator and propose feasible tuning parameter selection. Third, we develop pointwise inference methods based on undersmoothing…

  PublisherOA PDFDOI

• Binscatter regressions

  The Stata Journal Promoting communications on statistics and Stata · 2025-03-01 · 8 citations

  article1st authorCorresponding

  In this article, we introduce the package binsreg , which implements the binscatter methods developed by Cattaneo et al. (2024a, arXiv:2407.15276 [stat.EM]; 2024b, American Economic Review 114: 1488–1514). The package comprises seven commands: binsreg, binslogit, binsprobit, binsqreg, binstest binspwc , and binsregselect . The first four commands implement binscatter plotting, point estimation, and uncertainty quantification (confidence intervals and confidence bands) for least-squares linear bi…

  PublisherDOI

• Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption

  The Review of Economics and Statistics · 2025-04-21 · 5 citations

  article1st authorCorresponding

  Abstract We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predicted, which we call causal predictands, allowing for multiple treated units with treatment adoption at possibly different points in…

  PublisherDOI

• lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators

  The Journal of Open Source Software · 2025-03-07 · 1 citations

  articleOpen access1st authorCorresponding

  Conditional cumulative distribution functions (CDFs), conditional probability density functions (PDFs), and derivatives thereof, are important parameters of interest in statistics, econometrics, and other data science disciplines. The package lpcde implements new estimation and inference methods for conditional CDFs, conditional PDFs, and derivatives thereof, employing the kernelbased local polynomial smoothing approach introduced in Cattaneo et al. (2024a).

  PublisherOA PDFDOI

Recent grants

• Conference: Statistical Foundations of Data Science and their Applications

  NSF · $25k · 2023–2024

• Statistical Methods for Ultrahigh-dimensional Biomedical Data

  NIH · $293k · 2006–2023

• Partitioning-Based Learning Methods for Treatment Effect Estimation and Inference

  NSF · $453k · 2023–2026

Frequent coauthors

• Michael Jansson

  University of California, Berkeley

  154 shared

• Rocío Titiunik

  65 shared

• Max H. Farrell

  47 shared

• Richard K. Crump

  Federal Reserve Bank of New York

  43 shared

• Sebastián Calónico

  Columbia University

  41 shared

• Xinwei Ma

  Pennsylvania State University

  24 shared

• Yingjie Feng

  24 shared

• Max Farrell

  University of California, Berkeley

  17 shared

Labs

• Matias D. Cattaneo's LabPI

  Research spans econometrics, statistics, machine learning, artificial intelligence, and causal inference.

Education

• Doctor of Philosophy, Economics

  University of California, Berkeley

  2008

• Master of Arts, Statistics

  University of California, Berkeley

  2005

• Master of Arts (Economics)

  Universidad Torcuato Di Tella

  2003

• Licentiate in Economics

  Universidad de Buenos Aires

  2000

Awards & honors

• 2026 Guggenheim Fellowship in Data Science
• Elected Member of the International Statistical Institute
• Fellow of the American Statistical Association
• Fellow of the Institute of Mathematical Statistics
• Fellow of the International Association for Applied Economet…

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