11/16/2017 | 4:15pm | E51-335
Reception to follow.
Abstract: There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In this talk, I introduce two new approaches for estimating individual treatment effect (ITE) from observational data (also known as uplift modeling). The first approach is based on using deep neural networks to learn a representation of the data that makes treated and control distributions look similar, and is motivated by a new theoretical result that provides an upper bound on the ITE error. The second approach tackles the more challenging problem of inferring causal effects in the presence of hidden confounding, building on recent advances in latent variable modeling (e.g., variational autoencoders). Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.
Bio: David Sontag joined MIT in January 2017 as Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) and Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Sontag’s research focuses on machine learning and artificial intelligence; at IMES, he leads a research group that aims to use machine learning to transform health care. Previously, he was an assistant professor in computer science and data science at New York University’s Courant Institute of Mathematical Sciences and a postdoctoral researcher at Microsoft Research New England. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.