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Discovering Fully Oriented Causal Networks

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conference contribution
posted on 2023-11-29, 18:15 authored by Osman Mian, Alexander Marx, Jilles VreekenJilles Vreeken
We study the problem of inferring causal graphs from observational data. We are particularly interested in discovering graphs where all edges are oriented, as opposed to the partially directed graph that the state of the art discover. To this end we base our approach on the algorithmic Markov condition. Unlike the statistical Markov condition, it uniquely identifies the true causal network as the one that provides the simplest---as measured in Kolmogorov complexity---factorization of the joint distribution. Although Kolmogorov complexity is not computable, we can approximate it from above via the Minimum Description Length principle, which allows us to define a consistent and computable score based on non-parametric multivariate regression. To efficiently discover causal networks in practice, we introduce the Globe algorithm, which greedily adds, removes, and orients edges such that it minimizes the overall cost. Through an extensive set of experiments we show Globe performs very well in practice, beating the state of the art by a margin.


Preferred Citation

Osman Mian, Alexander Marx and Jilles Vreeken. Discovering Fully Oriented Causal Networks. In: National Conference of the American Association for Artificial Intelligence (AAAI). 2021.

Primary Research Area

  • Empirical and Behavioral Security

Secondary Research Area

  • Trustworthy Information Processing

Name of Conference

National Conference of the American Association for Artificial Intelligence (AAAI)

Legacy Posted Date


Open Access Type

  • Unknown


@inproceedings{cispa_all_3316, title = "Discovering Fully Oriented Causal Networks", author = "Mian, Osman and Marx, Alexander and Vreeken, Jilles", booktitle="{National Conference of the American Association for Artificial Intelligence (AAAI)}", year="2021", }

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