Uncertainty in Artificial Intelligence
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Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming
Antti Hyttinen, Frederick Eberhardt, Matti Jarvisalo
Abstract:
Recent approaches to causal discovery based on Boolean satisfiability solvers have opened new opportunities to consider search spaces for causal models with both feedback cycles and unmea- sured confounders. However, the available meth- ods have so far not been able to provide a prin- cipled account of how to handle conflicting con- straints that arise from statistical variability. Here we present a new approach that preserves the ver- satility of Boolean constraint solving and attains a high accuracy despite the presence of statisti- cal errors. We develop a new logical encoding of (in)dependence constraints that is both well suited for the domain and allows for faster solv- ing. We represent this encoding in Answer Set Programming (ASP), and apply a state-of-the- art ASP solver for the optimization task. Based on different theoretical motivations, we explore a variety of methods to handle statistical errors. Our approach currently scales to cyclic latent variable models with up to seven observed vari- ables and outperforms the available constraint- based methods in accuracy.
Keywords:
Pages: 340-349
PS Link:
PDF Link: /papers/14/p340-hyttinen.pdf
BibTex:
@INPROCEEDINGS{Hyttinen14,
AUTHOR = "Antti Hyttinen and Frederick Eberhardt and Matti Jarvisalo",
TITLE = "Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "340--349"
}


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