Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming

Antti Hyttinen California Institute of Technology, Frederick Eberhardt Caltech, Matti Järvisalo HIIT/University of Helsinki
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:27-36, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-technology14a, title = {Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming}, author = {Technology, Antti Hyttinen California Institute of and Caltech, Frederick Eberhardt and Helsinki, Matti J{\"a}rvisalo HIIT/University of}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {27--36}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/technology14a/technology14a.pdf}, url = {https://proceedings.mlr.press/r12/technology14a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming %A Antti Hyttinen California Institute of Technology %A Frederick Eberhardt Caltech %A Matti Järvisalo HIIT/University of Helsinki %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-technology14a %I PMLR %P 27--36 %U https://proceedings.mlr.press/r12/technology14a.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Technology, A.H.C.I.o., Caltech, F.E. & Helsinki, M.J.H.o.. (2014). Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:27-36 Available from https://proceedings.mlr.press/r12/technology14a.html. Reissued by PMLR on 04 October 2026.

Related Material