Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, where he is also an Associated Faculty in the Department of Economics, the Center for Statistics and Machine Learning (CSML), and the Program in Latin American Studies (PLAS). His research spans econometrics, statistics, data science and decision science, with particular interests in program evaluation and causal inference. Most of his work is interdisciplinary and motivated by quantitative problems in the social, behavioral, and biomedical sciences. As part of his main research agenda, he has developed novel semi-/non-parametric, high-dimensional, and machine learning inference procedures with demonstrably superior robustness to tuning parameter and other implementation choices. Matias was elected Fellow of the Institute of Mathematical Statistics (IMS) in 2022. He also serves in the editorial boards of the Journal of the American Statistical Association, Econometrica, Operations Research, Econometric Theory, the Econometrics Journal, and the Journal of Causal Inference. In addition, Matias is an Amazon Scholar, and has advised several governmental, multilateral, non-profit, and for-profit organizations around the world.
Matias earned a Ph.D. in Economics in 2008 and an M.A. in Statistics in 2005 from the University of California at Berkeley. He also completed an M.A. in Economics at Universidad Torcuato Di Tella in 2003 and a B.A. in Economics at Universidad de Buenos Aires in 2000. Prior to joining Princeton University in 2019, he was a faculty member in the departments of economics and statistics at the University of Michigan.
Matias was born and raised in Buenos Aires, Argentina. He is married to Rocio Titiunik, and they have two daughters.
Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimaxoptimal uniform convergence rate of the dyadic kernel density estimator, along with strong approximation results for the associated standardized and Studentized t-processes. A consistent variance estimator enables the construction of valid and feasible uniform confidence bands for the unknown density function. A crucial feature of dyadic distributions is that they may be “degenerate” at certain points in the support of the data, a property making our analysis somewhat delicate. Nonetheless our methods for uniform inference remain robust to the potential presence of such points. For implementation purposes, we discuss procedures based on positive semi-definite covariance estimators, mean squared error optimal bandwidth selectors and robust bias-correction techniques. We illustrate the empirical finite-sample performance of our methods both in simulations and with real-world data. Our technical results concerning strong approximations and maximal inequalities are of potential independent interest. Keywords: dyadic data, networks, kernel density estimation, minimaxity, strong approximation.