SAT-Based Causal Discovery under Weaker Assumptions

Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:671-680, 2017.

Abstract

Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability (SAT) solvers, we en- code 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 condi- tions than are standardly assumed. This imple- mentation of a whole set of assumptions in the same platform enables us to systematically ex- plore the effect of weakening the Faithfulness assumption on causal discovery. An important effect, suggested by simulation results, is that adopting weaker assumptions greatly allevi- ates the problem of conflicting constraints and substantially shortens solving time. As a re- sult, SAT-based causal discovery is potentially more scalable under weaker assumptions.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-zhalama17a, title = {{SAT}-Based Causal Discovery under Weaker Assumptions}, author = {Zhalama and Zhang, Jiji and Eberhardt, Frederick and Mayer, Wolfgang}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {671--680}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/zhalama17a/zhalama17a.pdf}, url = {https://proceedings.mlr.press/r15/zhalama17a.html}, abstract = {Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability (SAT) solvers, we en- code 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 condi- tions than are standardly assumed. This imple- mentation of a whole set of assumptions in the same platform enables us to systematically ex- plore the effect of weakening the Faithfulness assumption on causal discovery. An important effect, suggested by simulation results, is that adopting weaker assumptions greatly allevi- ates the problem of conflicting constraints and substantially shortens solving time. As a re- sult, SAT-based causal discovery is potentially more scalable under weaker assumptions.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T SAT-Based Causal Discovery under Weaker Assumptions %A Zhalama %A Jiji Zhang %A Frederick Eberhardt %A Wolfgang Mayer %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-zhalama17a %I PMLR %P 671--680 %U https://proceedings.mlr.press/r15/zhalama17a.html %V R15 %X Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability (SAT) solvers, we en- code 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 condi- tions than are standardly assumed. This imple- mentation of a whole set of assumptions in the same platform enables us to systematically ex- plore the effect of weakening the Faithfulness assumption on causal discovery. An important effect, suggested by simulation results, is that adopting weaker assumptions greatly allevi- ates the problem of conflicting constraints and substantially shortens solving time. As a re- sult, SAT-based causal discovery is potentially more scalable under weaker assumptions. %Z Reissued by PMLR on 04 October 2026.
APA
Zhalama, , Zhang, J., Eberhardt, F. & Mayer, W.. (2017). SAT-Based Causal Discovery under Weaker Assumptions. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:671-680 Available from https://proceedings.mlr.press/r15/zhalama17a.html. Reissued by PMLR on 04 October 2026.

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