AFTLab Seminar: Generalized Random Forests, Stefan Wager, Stanford

Advance Financial Technologies Laboratory (AFTLab)

Title: Generalized Random Forests
Speaker: Stefan Wager, Stanford
Date: November 2, 2017
Time: 4:50pm
Location: Y2E2, Room 300

Abstract:
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method operates at a particular point in covariate space by considering a weighted set of nearby training examples; however, instead of using classical kernel weighting functions that are prone to a strong curse of dimensionality, we use an adaptive weighting function derived from a forest designed to express heterogeneity in the specified quantity of interest. We propose a flexible, computationally efficient algorithm for growing generalized random forests, develop a large sample theory for our method showing that our estimates are consistent and asymptotically Gaussian, and provide an estimator for their asymptotic variance that enables valid confidence intervals. We use our approach to develop new methods for three statistical tasks: non-parametric quantile regression, conditional average partial effect estimation, and heterogeneous treatment effect estimation via instrumental variables. A software implementation, grf for R and C++, is available from CRAN.

Bio:
Stefan Wager’s research lies at the intersection of causal inference, optimization, and statistical learning. He is particularly interested in developing new solutions to classical problems in statistics, economics and decision making that leverage recent developments in machine learning.Stefan Wager is an Assistant Professor of Operations, Information and Technology at Stanford University’s Graduate School of Business, and an Assistant Professor of Statistics (by courtesy). He received his Ph.D. in Statistics from Stanford University in 2016, and also holds a B.Sc. (2011) degree in Mathematics from Stanford. He was a postdoctoral researcher at Columbia University during the academic year 2016-2017, and has worked with or consulted for several Silicon Valley companies, including Dropbox, Facebook and Google.

Date: 
Thursday, November 2, 2017 - 4:50pm to 5:50pm
location: 
Jerry Yang and Akiko Yamazaki Environment and Energy Building (Y2E2), 473 Via Ortega, Stanford, CA 94305, USA