Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries, SAM aims to find the underlying causal structure from observational data. The approach is based on a game betweendifferent players...

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Bibliographic Details
Published in:Journal of machine learning research 2022-01, Vol.23 (219), p.1-62
Main Authors: Kalainathan, Diviyan, Goudet, Olivier, Guyon, Isabelle, Lopez-Paz, David, Sebag, Michèle
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Online Access:Get full text
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Summary:A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries, SAM aims to find the underlying causal structure from observational data. The approach is based on a game betweendifferent players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the generated data against the original data. A learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforcethe optimization of the graph structure and parameters through stochastic gradient descent. SAM is extensively experimentally validated on synthetic and real data.
ISSN:1532-4435
1533-7928