TILOS Seminar: Causal Discovery for Root Cause Analysis
Professor Murat Kocaoglu, Assistant Professor, Purdue University
Cause-effect relations are crucial for several fields, from medicine to policy design as they inform us of the outcomes of our actions a priori. However, causal knowledge is hard to curate for complex systems that might be changing frequently. Causal discovery algorithms allow us to extract causal knowledge from the available data. In this talk, first, we provide a short introduction to algorithmic causal discovery. Next, we propose a novel causal discovery algorithm from a collection of observational and interventional datasets in the presence of unobserved confounders, with unknown intervention targets. Finally, we demonstrate the effectiveness of our algorithm for root-cause analysis in microservice architectures.
Dr. Kocaoglu received his B.S. degree in Electrical-Electronics Engineering with a minor in Physics from the Middle East Technical University in 2010, his M.S. degree from the Koc University, Turkey in 2012, and his Ph.D. degree from The University of Texas at Austin in 2018 under the supervision of Prof. Alex Dimakis and Prof. Sriram Vishwanath. He was a Research Staff Member in the MIT-IBM Watson AI Lab in IBM Research, Cambridge, Massachusetts from 2018 to 2020. Since 2021, he is an assistant professor in the School of ECE at Purdue University. His current research interests include causal inference and discovery, causal machine learning, deep generative models, and information theory.