Justin Whitehouse

Email: jwhiteho (at) stanford (dot) edu

I am a SAIL postdoctoral fellow at Stanford University, where I am fortunate to work with Vasilis Syrgkanis and Ramesh Johari. My research is broadly focused on problems at the intersection of causal inference, machine learning, and optimal decision making. I am particularly interested in studying how classical estimation strategies for causal inference (doubly-robust/double ML methods) can be applied to modern ML tasks such as model calibration, policy learning/evaluation, and more. I am also interested in developing anytime-valid statistical methods, which focus on providing non-asymptotic confidence intervals under data-dependent stopping conditions. A more detailed outline for some of my interests is provided below.

Before starting as a postdoc at Stanford, I received my PhD in computer science from Carnegie Mellon University. There, I was advised by Aaditya Ramdas and Steven Wu. The bulk of my theoretical research was focused on developing anytime-valid methods and time-uniform concentration inequalities. I have applied my inequalities to a variety of ML-related/causal inferece-related problems, such as kernelized bandit learning, differentially private learning, and adaptive causal effect estimation in panel data/network interference settings. Prior to my PhD, I was an undergraduate at Columbia University in New York City. There, I majored in mathematics and computer science.


Publications and Preprints

Teaching

I have served as a teaching assistant for the following classes.