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Online Causal Inference Seminar

June 28, 2022 - 8:30am

Free and open to the public

Tuesday, June 28, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)




  • SpeakerSamuel Yang (Cornell University)

  • TitleUncertainty Quantification for Causal Discovery

  • DiscussantDaniel Malinsky (Columbia University)

  • AbstractCausal 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.

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