Proceedings

Bayesian Probabilities for Constraint-Based Causal Discovery

Tom Claassen, Tom Heskes

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2992-2995.

Abstract

We target the problem of accuracy and robustness in causal inference from finite data sets. Our aim is to combine the inherent robustness of the Bayesian approach with the theoretical strength and clarity of constraint-based methods. We use a Bayesian score to obtain probability estimates on the input statements used in a constraint-based procedure. These are subsequently processed in decreasing order of reliability, letting more reliable decisions take precedence in case of conflicts, until a single output model is obtained. Tests show that a basic implementation of the resulting Bayesian Constraint-based Causal Discovery (BCCD) algorithm already outperforms established procedures such as FCI and Conservative PC. It indicates which causal decisions in the output have high reliability and which do not. The approach is easily adapted to other application areas such as complex independence tests.

Tom Claassen, and Tom Heskes. 2013. Bayesian Probabilities for Constraint-Based Causal Discovery. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 2992–2995. https://ijcai.org/proceedings/2013/443
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