Confidence Sets for Causal Orderings

Presented by Y. Samuel Wang, Asst. Professor, Statistics and Data Science, Cornell University

Wednesday, November 13, 2024, 4:00 PM, AUST 202

Webex Meeting Link
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)

Abstract: Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. A primary challenge in causal discovery of directed acyclic graphs is determining a causal ordering among the variables, and our work offers a framework for constructing confidence sets of causal orderings that the data do not rule out. Our methodology specifically applies to identifiable structural equation models with additive errors and is based on a residual bootstrap procedure to test the goodness-of-fit of causal orderings. We demonstrate the asymptotic validity of the confidence set constructed using this goodness-of-fit test and explain how the confidence set may be used to form sub/supersets of ancestral relationships as well as confidence intervals for causal effects that incorporate model uncertainty.
Bio: DY. Samuel Wang (Sam) is an assistant professor in the Statistics and Data Science Department at Cornell University. Much of his research focuses on problems where the goal is to discover interpretable structure which underlies the data generating process. This includes problems in the areas of causal discovery, graphical models, and mixed membership models. He received his PhD from the University of Washington and completed a post-doc at the University of Chicago.