Y. Samuel Wang (Sam) is currently a post-doc at the University of Chicago’s Booth School of Business working with Mladen Kolar. He previously completed his PhD in Statistics at the University of Washington under the supervision of Mathias Drton and received his undergraduate degree in both applied math and economics at Rice University. He also worked as a management consultant before embarking on his PhD studies. His primary research area is graphical models and causal discovery, and his work in statistics and machine learning has appeared in venues such as the Annals of Statistics, Biometrika, Annals of Applied Statistics, and NeurIPS. In his free time, he enjoys playing soccer, attempting to cook, and riding his bike.
Talk: Causal discovery with non-Gaussian data
A link to this Zoom talk will be sent to the Stats Seminar list serv
Abstract: Randomized controlled trials (RCT) are the gold standard for identifying causal relationships; however, in many settings RCTs are unethical, impossible, or prohibitively expensive. Thus, the problem of causal discovery examines conditions and procedures which allow recovery of causal structure from observational data. Previous work by Shimizu et al. (2006) has shown that when the data are generated by a linear structural equation model (SEM) with non-Gaussian errors and no confounding, the causal structure can be identified from population values of the observational distribution. In this talk, we will consider the case when the system may contain latent confounding and show conditions under which the causal structure is still identifiable. Time permitting, we will also discuss some recent work on uncertainty quantification for causal discovery procedures.