Webinar Series: Mathematical Foundations of Data Science

On Binscatter

Speaker

Matias Cattaneo, (Princeton University)

Abstract

Binscatter methods are popular in economics and other social and behavioral sciences. They provide a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression, quantile and other nonparametric function estimation settings. They are also often used for informal evaluation of substantive hypotheses such as linearity or monotonicity of an unknown function or transformations thereof. We present an array of theoretical and practical results that aid both in understanding current practices (i.e., their validity or lack thereof) and in offering theory-based guidance for future applications. Our main results include principled number of bins selection, confidence intervals and bands, hypothesis tests for parametric and shape restrictions of the regression, quantile and other functions of interest, and other new methods applicable to both canonical binscatter as well as higher-order polynomial smoothness-restricted covariate-adjusted extensions thereof. From a technical perspective, novel theoretical results for linear and non-linear partitioning-based series estimation improving on existing literature are obtained. Companion general-purpose software packages for Stata and R are provided (https://nppackages.github.io/binsreg/).

Bio

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, Matias 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 is currently an Amazon Scholar, and he has advised several governmental, multilateral, non-profit, and for-profit organizations around the world. He also serves in the editorial boards of the Journal of the American Statistical Association, Econometrica, Operations Research, the Journal of Business & Economic Statistics, Econometric Theory, the Econometrics Journal, and the Journal of Causal Inference.

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.