Event Details:
Tuesday, June 28, 2022
8:30am - 9:30am PDT
This event is open to:
General Public
Free and open to the public
Tuesday, June 28, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)
- Speaker: Samuel Yang (Cornell University)
- Title: Uncertainty Quantification for Causal Discovery
- Discussant: Daniel Malinsky (Columbia University)
- Abstract: Causal discovery procedures are popular methods for discovering causal structure across the physical, biological, and social sciences. However, most procedures for causal discovery only output a single estimated causal model or single equivalence class of models. In this work, we propose a procedure for quantifying uncertainty in causal discovery. Specifically, we consider structural equation models where a unique graph can be identified and propose a procedure which returns a confidence sets of causal orderings which are not ruled out by the data. We show that asymptotically, a true causal ordering will be contained in the returned set with some user specified probability. In addition, the confidence set can be used to form conservative sets of ancestral relationships.
Related Topics
Explore More Events
-
Causal Science Center
Bay Area Tech Economics Seminar: The Impact of Generative AI on Jobs and Skills
-University of San Francisco, 2130 Fulton Street, San Francisco, CA 94117 -
Sustainability Data Science Center
Sustainability Data Science Conference
-353 Serra Mall, Gates Computer Science, 403 (Fujitsu) conference room, Stanford, CA 94305 -
Lecture
Data Feminism for AI
-John A. and Cynthia Fry Gunn Rotunda, E241 at the ChEM-H / Neuro, 290 Jane Stanford Way, 2nd floor, Stanford, CA 94305