University of Texas at Austin

Past Event: Babuška Forum

Discovering Causality in Data using Entropy

Alex Dimakis, Associate Professor, Electrical and Computer Engineering, UT Austin

10 – 11AM
Friday Oct 7, 2016

POB 6.304

Abstract

Causality has been studied under several frameworks in statistics and artificial intelligence. We will briefly survey Pearl’s Structural Equation model and explain how interventions can be used to discover causality. We will also present a novel information theoretic framework for discovering causal directions from observational data when interventions are not possible. The starting point is conditional independence in joint probability distributions and no prior knowledge on causal inference is required for this lecture. Bio Dr. Alex Dimakis is an Associate Professor in the Electrical & Computer Engineering department at The University of Texas at Austin. Prof. Dimakis received his Ph.D. in 2008 and M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech. He received an NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012.

Event information

Date
10 – 11AM
Friday Oct 7, 2016
Location POB 6.304
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