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.
Schedule
Mon, Mar 13: Lecture 1, 10 am – noon, Skiles 006 (Coffee and snacks provided)
Tue, Mar 14: Lecture 2, 10 am – noon, Groseclose 119 (Lunch provided)
General rules for deriving intervention distribution from the observational distribution (this generalizes the adjustment theorem)
Front door theorem
Learning Causal Models
Learning with infinite samples
Learning up to Markov equivalence (CPDAG)
Faithfulness
Algorithms for structure learning
PC Algorithm for CPDA
ICA algorithm for LiNGAM
Hidden Variables (Latent confounders)
Instrument variables and 2SLS method
Conditional Independence (CI) Testing
Hardness of CI testing
Partial correlation coefficient
Kernel based methods
Conditional randomization
Classifier based testing
Bio: 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.
