[Bnc-faculty-all-list] Webinar by Prof. Murat Kocaoglu, Purdue: March 4 10AM-11AM

[Gupta, Sumeet Kumar]

Hello

It is my great pleasure to announce Prof. Murat Kocaoglu's talk on March 4, 2021 from 10AM-11AM in the Seminar Series for ECE Faculty and Students. The details are below and attached. Hope to see you there.

Thanks

Sumeet

Zoom Link: https://purdue-edu.zoom.us/j/3164232249
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Title:   Entropic Methods for Causal Discovery

Abstract: Causality is a fundamental concept in multiple disciplines. Causal questions arise in fields ranging from medical research to engineering, philosophy to physics. The last few decades have witnessed the development of a mathematical model of probabilistic causation by Judea Pearl and many others. In this modeling framework, directed acyclic graphs - called the causal graphs - arise as natural objects to capture causal relations between random variables.
     A fundamental problem is to learn the causal graph over a pair of discrete variables X, Y from data. In this talk, we first give a short summary of Pearl's framework for modeling causal relations. Next, we introduce the entropic causal inference framework which demonstrates that under some assumptions it is possible to identify the causal graph from observational data.
    First, we consider the setting without latent confounders and show that if the amount of exogenous randomness is small, it is possible to identify if X causes Y or Y causes X. In the next setting, we allow a latent confounder between the two observed variables. We show that a similar identifiability result arises if the latent confounder has small entropy. Specifically, we show that it is possible to identify if one variable causes the other or if the observed dependence is solely due to the latent confounder. In both settings, entropic causality framework establishes connections between causal discovery and information theory. Using this connection, we propose efficient algorithms for learning the underlying causal graph from observational data.

Speaker Bio: Murat Kocaoglu received his B.S. degree in Electrical - Electronics Engineering with a minor degree in Physics from the Middle East Technical University in 2010, and M.S. degree from Koc University, Turkey in 2012 under the supervision of Prof. Ozgur B. Akan and Ph.D. degree from The University of Texas at Austin in 2018 under the supervision of Prof. Alex Dimakis and Prof. Sriram Vishwanath. He worked as a Research Staff Member in the MIT-IBM Watson AI Lab in IBM Research, Cambridge, Massachusetts from 2018 to 2020. He is currently an assistant professor at Purdue University in the School of Electrical and Computer Engineering. His current research interests include causal inference, generative adversarial networks, and information theory.
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