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
