Causal Inference Course (Day 5)

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Moving away from decision-making based on observed correlations in data, causal inference develops the mathematical foundations for reasoning about the direction of implication — aka cause and effect – for observed dependencies in data. These foundations lead to tools and techniques that can be used for improved models and better decision-making for emerging data-driven systems. This short course covers the motivation, mathematical foundations, and machine learning algorithms for causal reasoning.

Speaker Biography: Sanjay Shakkottai received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign in 2002. Shakkottai is a professor in the Engineering department at University of Texas at Austin and holds the Cockrell Family Chair in Engineering #15. He received the NSF CAREER award (2004) and was elected as an IEEE Fellow in 2014. He was a co-recipient of the IEEE Communications Society William R. Bennett Prize in 2021 and is currently the Editor in Chief of IEEE/ACM Transactions on Networking. Shakkottai’s research interests lie at the intersection of algorithms for resource allocation, statistical learning and networks, with applications to wireless communication networks and online platforms.

  1. AI4OPT - AI Institute for Advances in Optimization
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