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Learning a causal structure: a Bayesian random graph approach

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Abstract

A Random Graph is a random object which takes its values in a space of graphs. We take advantage of the expressibility of graphs in order to model uncertainty about the existence of causal relations within a given set of variables. We adopt a Bayesian point of view which leads us to propose a belief updating procedure with the objective of capturing a causal structure via interaction with a causal environment. Besides learning a causal structure, our proposal is also able to learn optimal actions, i.e., an optimal policy. We test our method in two experiments, each on a different scenario. Our experiments confirm that the proposed technique is able to learn a causal structure as well as an optimal policy. On the other hand, the second experiment shows that our proposal manages to learn an underlying causal model within several tasks in which different configurations of the causal structure are used.

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Funding

Mauricio Gonzalez-Soto is supported by the Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship number 487868 and project number CB2017-2018 A1-S-43346. Ivan Feliciano-Avelino is supported by the Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship number 725976 and project number CB2017-2018 A1-S-43346. L. Enrique Sucar is supported by CONACyT project number CB2017-2018 A1-S-43346.

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Authors and Affiliations

  1. Coordinación de Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonanzintla, Puebla, Mexico

    Mauricio Gonzalez-Soto, Ivan Feliciano-Avelino, L. Enrique Sucar & Hugo Jair Escalante

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  1. Mauricio Gonzalez-Soto
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  2. Ivan Feliciano-Avelino
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  3. L. Enrique Sucar
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  4. Hugo Jair Escalante
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Correspondence to Mauricio Gonzalez-Soto.

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All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Code for this paper can be found at: https://github.com/ivanfeliciano/playing-against-nature

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Gonzalez-Soto, M., Feliciano-Avelino, I., Sucar, L.E. et al. Learning a causal structure: a Bayesian random graph approach. Neural Comput & Applic 35, 18147–18159 (2023). https://doi.org/10.1007/s00521-021-06506-5

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  • DOI: https://doi.org/10.1007/s00521-021-06506-5

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