Time+Place: Tuesday 03/01/2017 10:30 Room 601 Taub Bld.
Title: Learning to act from observational data
Speaker: Uri Shalit - CS-Lecture - Note unusual hour and place http://www.cs.nyu.edu/~shalit/
Affiliation: Courant Institute of Mathematical Sciences, New York University


The proliferation of data collection in the health, commercial, and 
economic spheres, brings with it opportunities for extracting new 
knowledge with concrete policy implications. Examples include 
individualizing medical practices based on electronic healthcare 
records, and understanding the implications of job training programs on 
employment and income.

The scientific challenge lies in the fact that standard prediction 
models such as supervised machine learning are often not enough for 
decision making from this so-called ''observational data'': 
Supervised learning does not take into account causality, nor does 
it account for the feedback loops that arise when predictions 
are turned into actions. On the other hand, existing causal-inference 
methods are not adapted to dealing with the rich and complex data now 
available, and often focus on populations, as opposed to 
individual-level effects.

The problem is most closely related to reinforcement learning and bandit 
problems in machine learning, but with the important property of having 
no control over experiments and no direct access to the actor's 

In my talk I will discuss how we apply recent ideas from machine 
learning to individual-level causal-inference and action. I will
introduce a novel generalization bound for estimating individual-level 
treatment effect, and further show how we use representation learning 
and deep temporal generative models to create new algorithms geared 
towards this problem. Finally, I will show experimental results using 
data from electronic medical records and data from a job training 

Short Bio:
Uri Shalit is a postdoctoral researcher in the Courant Institute of 
Mathematical Sciences, New York University, working at David Sontag's 
Clinical Machine Learning Lab. His research is focused on creating new 
methods for finding causal relationships in large-scale high-dimensional 
observational studies. One of the major motivations for his research is 
applications in healthcare and clinical medicine. Uri completed his PhD 
studies at the School of Computer Science & Engineering at The Hebrew 
University of Jerusalem, under the guidance of Prof. Gal Chechik and 
Prof. Daphna Weinshall. From 2011 to 2014 Uri was a recipient of 
Google's European Fellowship in Machine Learning. Previously he has 
received the Daniel Amit fellowship for significant contribution in 
theoretical or computational neuroscience, and the Alice and Jack Ormut 
Foundation PhD Fellowship.