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Primary Submission Category: Causal Discovery

Confidence Sets for Causal Discovery

Authors: Y. Samuel Wang, Mladen Kolar, Mathias Drton,

Presenting Author: Y. Samuel Wang*

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 aim to quantify 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 set 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 as well as confidence intervals for causal effects which account for model uncertainty.