Max Farrell

Max Farrell is an Associate Professor of Economics and the Mellichamp Chair of Mind and Machine Intelligence at the University of California, Santa Barbara (UCSB), within the Department of Economics of the College of Letters & Science. He is also an Amazon Scholar and serves as an associate editor for the Review of Economics and Statistics and Journal of Applied Econometrics, with editorial board membership at the Journal of Machine Learning Research. His research focuses on machine learning, high-dimensional data, semiparametric/nonparametric methods, and causal inference, with applications in econometrics and statistics.

Farrell holds a Ph.D. in Economics and M.A. in Statistics from the University of Michigan, and S.B. degrees in Mathematics and Economics from MIT. His work emphasizes developing novel statistical methods, such as open-source software tools like rdrobust (for regression discontinuity designs), binsreg (binscatter regression), and nprobust (nonparametric kernel-based estimation). These tools have been recognized for their practical impact in applied research.

His research trends span regression discontinuity designs, deep learning applications in causal inference, and nonlinear binning methods for visualization and estimation. His recent work explores individual heterogeneity in treatment effects and high-dimensional econometric models. Farrell’s software contributions have been widely adopted in empirical economics and statistics.

Scientific Awards: His software packages have received awards for their innovation and utility in statistical analysis.

Advising & Grants: While no students are explicitly listed here, his software development and editorial roles reflect significant contributions to the field. His work is supported by grants from Amazon and UCSB’s Mellichamp Chair.

Labs/Teams: Collaborates extensively with researchers like Matias Cattaneo, Tengyuan Liang, and Sebastian Calonico on projects in econometrics and machine learning.