SAT-based causal discovery under weaker assumptions

Zhalama, Jiji ZHANG, Frederick EBERHARDT, Wolfgang MAYER

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

7 Citations (Scopus)


Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability (SAT) solvers, we encode a variety of assumptions that weaken the Faithfulness assumption. The encoding results in a number of SAT-based algorithms whose asymptotic correctness relies on weaker conditions than are standardly assumed. This implementation of a whole set of assumptions in the same platform enables us to systematically explore the effect of weakening the Faithfulness assumption on causal discovery. An important effect, suggested by simulation results, is that adopting weaker assumptions greatly alleviates the problem of conflicting constraints and substantially shortens solving time. As a result, SAT-based causal discovery is potentially more scalable under weaker assumptions.

Original languageEnglish
Title of host publicationProceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI)
PublisherAssociation for Uncertainty in Artificial Intelligence (AUAI)
Publication statusPublished - 1 Jan 2017
Event33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 - Sydney, Australia
Duration: 11 Aug 201715 Aug 2017


Conference33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017
Abbreviated titleUAI 2017


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