Title

SAT-based causal discovery under weaker assumptions

Document Type

Book chapter

Source Publication

Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI)

Publication Date

2017

Publisher

Association for Uncertainty in Artificial Intelligence (AUAI)

Abstract

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.

Publisher Statement

Copyright © 2017 Uncertainty in Artificial Intelligence. Access to external full text or publisher's version may require subscription.

Additional Information

Advance online publication.

Full-text Version

Publisher’s Version

Language

English

Recommended Citation

Zhalama, Zhang, J., Eberhardt, F. & Mayer, W. (2017). SAT-based causal discovery under weaker assumptions. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). Advance online publication. Retrieved from: http://auai.org/uai2017/proceedings/papers/234.pdf

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