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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.
|Title of host publication||Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI)|
|Publisher||Association for Uncertainty in Artificial Intelligence (AUAI)|
|Publication status||Published - 1 Jan 2017|
|Event||33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 - Sydney, Australia|
Duration: 11 Aug 2017 → 15 Aug 2017
|Conference||33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017|
|Abbreviated title||UAI 2017|
|Period||11/08/17 → 15/08/17|
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- 1 Finished
Causation, Decision, and Imprecise Probabilities
ZHANG, J. & SEIDENFELD, T.
Research Grants Council (HKSAR)
1/01/16 → 31/12/17
Project: Grant Research