Fwd: FW: Statistics/StatML and Societal Problems Job Talk Candidate _Anish Agarwal_Monday, 2/1/2022 at noon

[Artur Dubrawski]

HeinzCollege activity is not always populated via the SCS channels, and in
this case it may be of interest to some of the Autonians.

Cheers
Artur

---------- Forwarded message ---------

*From:* Heinz-faculty <heinz-faculty-bounces+js1m=
andrew.cmu.edu at lists.andrew.cmu.edu> *On Behalf Of *Natalia Pascal
*Sent:* Tuesday, January 25, 2022 11:06 AM
*To:* heinz-faculty at lists.andrew.cmu.edu; heinz-phd at lists.andrew.cmu.edu
*Cc:* Amelia M Haviland <amelia at andrew.cmu.edu>; Irina Novikova <
irinasn at andrew.cmu.edu>
*Subject:* Statistics/StatML and Societal Problems Job Talk Candidate
_Anish Agarwal_Monday, 2/1/2022 at noon



Hello All!



*Anish Agarwal, *Ph.D., EECS, MIT is scheduled to present* on Monday,
February 1st, 2022* *at noon – 1:20 PM, via zoom, *as a part of the
Assistant Professor (Tenure Track) - Statistics/StatML and Societal
Problems faculty search.



Join Zoom Meeting
https://cmu.zoom.us/j/99684715715?pwd=VWlaT2tqemYvdFpVVlBObjk5QTBoQT09

Meeting ID: 996 8471 5715
Passcode: 569553



*Anish Brief Bio:*

Anish is currently a postdoctoral fellow at the Simons Institute at UC
Berkeley. He did his PhD at MIT in EECS where he was advised by Alberto
Abadie, Munther Dahleh, and Devavrat Shah. His research focuses on
designing and analyzing methods for causal machine learning, and applying
it to critical problems in social and engineering systems. He currently
serves as a technical consultant to TauRx Therapeutics and Uber
Technologies on questions related to experiment design and causal
inference. Prior to the PhD, he was a management consultant at Boston
Consulting Group. He received his BSc and MSc at Caltech.



*Title: Causal Inference for Socio-Economic and Engineering Systems*



*Abstract:*

*What will happen to Y if we do A? *

A variety of meaningful socio-economic and engineering questions can be
formulated this way. To name a few: What will happen to a patient’s health
if they are given a new therapy? What will happen to a country’s economy if
policy-makers legislate a new tax? What will happen to a company’s revenue
if a new discount is introduced? What will happen to a data center’s
latency if a new congestion control protocol is used? In this talk, we will
explore how to answer such counterfactual questions using observational
data---which is increasingly available due to digitization and pervasive
sensors---and/or very limited experimental data. The two key challenges in
doing so are: (i) counterfactual prediction in the presence of latent
confounders; (ii) estimation with modern datasets which are
high-dimensional, noisy, and sparse.



Towards this goal, the key framework we introduce is connecting causal
inference with tensor completion, a very active area of research across a
variety of fields. In particular, we show how to represent the various
potential outcomes (i.e., counterfactuals) of interest through an order-3
tensor. The key theoretical results presented are: (i) Formal
identification results establishing under what missingness patterns, latent
confounding, and structure on the tensor is recovery of unobserved
potential outcomes possible. (ii) Introducing novel estimators to recover
these unobserved potential outcomes and proving they are finite-sample
consistent and asymptotically normal.

The efficacy of the proposed estimators is shown on high-impact real-world
applications. These include working with: (i) TaurRx Therapeutics to
propose novel clinical trial designs to reduce the number of patients
recruited for a trial and to correct for bias from patient dropouts. (ii)
Uber Technologies on evaluating the impact of certain driver engagement
policies without having to run an A/B test. (iii) U.S. and Indian
policy-makers to evaluate the impact of mobility restrictions on COVID-19
mortality outcomes. (iv) The Poverty Action Lab (J-PAL) at MIT to make
personalized policy recommendations to improve childhood immunization rates
across different villages in Haryana, India.

Finally, we discuss connections between causal inference, tensor
completion, and offline reinforcement learning.

*Please reach out with any questions to the head of the committee:
Professor* Amelia M Haviland, amelia at andrew.cmu.edu



We hope to see you there!



*Natalia Pascal*

Research and Administrative Coordinator

Carnegie Mellon University

Heinz College of Information Systems and Public Policy

412.268.7856; E-mail: npascal at andrew.cmu.edu
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