Max Farrell

About

Max H. Farrell is an Associate Professor of Economics at the University of California, Santa Barbara, holding the Mellichamp Chair of Mind and Machine Intelligence. His research focuses on econometrics, particularly in the areas of regression discontinuity designs, nonparametric inference, and the application of deep learning in economics.

Research topics

• Political Science
• Mathematics
• Applied mathematics
• Statistics

Selected publications

• On Binscatter

  American Economic Review · 2024 · 115 citations

  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

• Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands.

  arXiv (Cornell University) · 2018-09-26 · 44 citations

  articleOpen access1st authorCorresponding

  We study deep neural networks and their use in semiparametric inference. We prove valid inference after first-step estimation with deep learning, a result new to the literature. We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), obtain valid semiparametric inference. Our estimation rates and semiparametric inference results handle the current standard architecture: fully connected feedforward neural n…

  Publisher

• On Binscatter

  arXiv (Cornell University) · 2019-02-25 · 27 citations

  preprintOpen access

  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…

  PublisherOA PDFDOI

• Binscatter Regressions

  arXiv (Cornell University) · 2019-02-25 · 23 citations

  preprintOpen access

  We introduce the package Binsreg, which implements the binscatter methods developed by Cattaneo, Crump, Farrell, and Feng (2024b,a). The package includes 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 binscatter regression (binsreg) and for nonlinear binscatter regression (bins…

  PublisherOA PDFDOI

• Binscatter regressions

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

  articleCorresponding

  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

Frequent coauthors

• Matias D. Cattaneo

  47 shared

• Sebastián Calónico

  Columbia University

  24 shared

• Yingjie Feng

  11 shared

• Richard K. Crump

  Federal Reserve Bank of New York

  8 shared

• Rocío Titiunik

  7 shared

• Tengyuan Liang

  6 shared

• Sanjog Misra

  University of Chicago

  6 shared

• Ernst Schaumburg

  Indian School of Business

  5 shared

Similar researchers at University of California, Santa Barbara

• Peter Kuhnh-35
• Kelly Bedardh-26
• Erik Eysterh-23