Selected talks on measurement, evaluation, and causal inference in machine learning.
Talk, Joint Statistical Meetings (JSM), Portland, OR, USA
Talk on doubly-robust estimation of dose-response functions for continuous treatments via double machine learning. Linked: simulation code.
Talk, Causal AI Conference San Francisco, San Francisco, CA, USA
Invited Speaker, Causal AI Conference. Overview of Causal AI Frameworks: Predictive vs Causal Approaches to Machine Learning
Talk, INFORMS Annual Meeting, Phoenix, AZ, USA
Presentation of joint work with Itai Ater, Brad Ross, Adi Shany, and Shoshana Vasserman on a large-scale field experiment in usage-based congestion pricing. Linked: working paper.
Talk, ACM WWW, Special Track on Web for Good, Online
Bayesian Machine Learning Models — an application to estimating advertiser valuations in online ad auctions. Companion talk to the WWW ‘22 paper Regulating Online Political Advertising.
Poster, MIT Conference on Digital Experimentation (CODE@MIT), Online
Poster presentation: an optimization-based approach to minimize contamination and maximize information gain for experiments on networks. Linked: code and writeup repository.