<div dir="ltr">HeinzCollege activity is not always populated via the SCS channels, and in this case it may be of interest to some of the Autonians.<div><br></div><div>Cheers</div><div>Artur </div><div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br></div><div lang="EN-US" link="#0563C1" vlink="#954F72"><div class="m_-521624245520465070WordSection1">
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<p class="MsoNormal"><b>From:</b> Heinz-faculty <heinz-faculty-bounces+js1m=<a href="mailto:andrew.cmu.edu@lists.andrew.cmu.edu" target="_blank">andrew.cmu.edu@lists.andrew.cmu.edu</a>>
<b>On Behalf Of </b>Natalia Pascal<br>
<b>Sent:</b> Tuesday, January 25, 2022 11:06 AM<br>
<b>To:</b> <a href="mailto:heinz-faculty@lists.andrew.cmu.edu" target="_blank">heinz-faculty@lists.andrew.cmu.edu</a>; <a href="mailto:heinz-phd@lists.andrew.cmu.edu" target="_blank">heinz-phd@lists.andrew.cmu.edu</a><br>
<b>Cc:</b> Amelia M Haviland <<a href="mailto:amelia@andrew.cmu.edu" target="_blank">amelia@andrew.cmu.edu</a>>; Irina Novikova <<a href="mailto:irinasn@andrew.cmu.edu" target="_blank">irinasn@andrew.cmu.edu</a>><br>
<b>Subject:</b> Statistics/StatML and Societal Problems Job Talk Candidate _Anish Agarwal_Monday, 2/1/2022 at noon<u></u><u></u></p>
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<p class="MsoNormal"><span style="font-size:12.0pt">Hello All!<u></u><u></u></span></p>
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<p><b><span style="font-family:"Calibri",sans-serif">Anish Agarwal, </span></b><span style="font-family:"Calibri",sans-serif">Ph.D., EECS, MIT is scheduled to present<b> on Monday, February 1st, 2022</b>
<b>at noon – 1:20 PM, via zoom, </b>as a part of the Assistant Professor (Tenure Track) - Statistics/StatML and Societal Problems faculty search.<u></u><u></u></span></p>
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<p><span style="font-family:"Calibri",sans-serif">Join Zoom Meeting</span> <br>
<span style="font-family:"Calibri",sans-serif"><a href="https://cmu.zoom.us/j/99684715715?pwd=VWlaT2tqemYvdFpVVlBObjk5QTBoQT09" target="_blank">https://cmu.zoom.us/j/99684715715?pwd=VWlaT2tqemYvdFpVVlBObjk5QTBoQT09</a></span>
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<p><span style="font-family:"Calibri",sans-serif">Meeting ID: 996 8471 5715</span>
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<span style="font-family:"Calibri",sans-serif">Passcode: 569553</span> <u></u><u></u></p>
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<p class="MsoNormal" style="text-align:justify"><b><span style="font-size:12.0pt;color:black">Anish Brief Bio:<u></u><u></u></span></b></p>
<p class="MsoNormal" style="text-align:justify"><span style="font-size:12.0pt">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.
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<p class="MsoNormal" style="text-align:justify"><b><span style="font-size:12.0pt;color:black">Title: Causal Inference for Socio-Economic and Engineering Systems<u></u><u></u></span></b></p>
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<p class="MsoNormal" style="text-align:justify"><b><span style="font-size:12.0pt;color:black">Abstract:<u></u><u></u></span></b></p>
<p class="MsoNormal" style="text-align:justify"><i><span style="font-size:12.0pt;color:black">What will happen to Y if we do A? </span></i><span style="font-size:12.0pt;color:black"><u></u><u></u></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="font-size:12.0pt;color:black">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.
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<p class="MsoNormal" style="text-align:justify"><span style="font-size:12.0pt;color:black">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. <u></u><u></u></span></p>
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<span style="font-size:12.0pt;color:black">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.<u></u><u></u></span></p>
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<span style="font-size:12.0pt;color:black">Finally, we discuss connections between causal inference, tensor completion, and offline reinforcement learning. <u></u><u></u></span></p>
<p class="MsoNormal"><strong><span style="font-size:12.0pt;font-family:"Calibri",sans-serif;font-weight:normal">Please reach out with any questions to the head of the committee: Professor</span></strong><span style="font-size:12.0pt"> Amelia M Haviland,
<a href="mailto:amelia@andrew.cmu.edu" target="_blank">amelia@andrew.cmu.edu</a><u></u><u></u></span></p>
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<p class="m_-521624245520465070Default"><span style="font-family:"Calibri",sans-serif;color:windowtext">We hope to see you there!</span><span style="color:windowtext"><u></u><u></u></span></p>
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<p class="MsoNormal"><b><i><span style="color:#002060">Natalia Pascal<u></u><u></u></span></i></b></p>
<p class="MsoNormal"><span style="color:#002060">Research and Administrative Coordinator<u></u><u></u></span></p>
<p class="MsoNormal"><span style="color:#c00000">Carnegie Mellon University<u></u><u></u></span></p>
<p class="MsoNormal"><span style="color:#002060">Heinz College of Information Systems and Public Policy<u></u><u></u></span></p>
<p class="MsoNormal"><span style="color:#002060">412.268.7856; E-mail: <a href="mailto:npascal@andrew.cmu.edu" target="_blank">
npascal@andrew.cmu.edu</a><u></u><u></u></span></p>
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