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C3.ai DTI Fall Colloquium: Causal Tensor Estimation

Event Type
Seminar/Symposium
Sponsor
C3.ai Digital Transformation Institute
Location
https://berkeley.zoom.us/webinar/register/WN_NdorsKMzRjGgy11RLyTkaw
Virtual
wifi event
Date
Sep 16, 2021   3:00 - 4:00 pm  
Views
5
Originating Calendar
NCSA-related events

Speaker: Devavrat Shah, Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Abstract:
In this talk, we present a framework for causal inference for the “panel” or “longitudinal” setting from the lens of tensor estimation. Traditionally, such panel or longitudinal settings are considered in econometrics literature for program or policy evaluation. Tensor estimation has been considered in machine learning, where tantalizing statistical and computational tradeoffs have emerged for random observation models. We introduce a causal variant of tensor estimation that provides a unified view for prior works in econometrics and provides newer avenues to explore. We discuss a method for estimating such a causal variant of the tensor and various exciting directions for future research, including offline reinforcement learning. This is based on joint work with Alberto Abadie (MIT), Anish Agarwal (MIT), and Dennis Shen (MIT/UC Berkeley).

Speaker Bio:
Shah is a professor in the Department of Electrical Engineering and Computer Science at MIT. His current research interests are at the interface of statistical inference and social data processing. His work has been recognized through prize paper awards in machine learning, operations research, and computer science, as well as career prizes including the 2010 Erlang prize from the INFORMS Applied Probability Society and the 2008 ACM Sigmetrics Rising Star Award. He is a distinguished alumnus of his alma mater IIT Bombay. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data.

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