Abstract

Causal knowledge is central to solving complex decision-making problems in many fields from engineering, and medicine to cyber physical systems. Causal inference has also recently been identified as a key capability to remedy some of the issues modern machine learning systems suffer from, from explainability and fairness to generalization. In this talk, we first provide a short introduction to probabilistic causal inference. Next, we discuss how deep neural networks can be used to obtain a representation of the causal system and help solve complex, high-dimensional causal inference problems with deep generative models. We will also discuss some machine learning applications of the proposed algorithms.

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 the Koc University, Turkey in 2012 and Ph.D. degree from The University of Texas at Austin in 2018. He was a Research Staff Member at the MIT -IBM Watson AI Lab in IBM Research, Cambridge, Massachusetts from 2018 to 2020. He is currently an assistant professor in the Elmore Family School of Electrical and Computer Engineering, Department of Computer Science (by courtesy) and Department of Statistics (by courtesy) at Purdue University where he leads the CausalML Lab. He received the Adobe Data Science Research Award in 2022, NSF CAREER Award in 2023, and Amazon Research Award in 2024. His current research interests include causal inference, deep generative models, and information theory.