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SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning

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Abstract

We present the Structural Agnostic Model (SAM), a framework to estimate end-to-end non-acyclic causal graphs from observational data. In a nutshell, SAM implements an adversarial game in which a separate model generates each variable, given real values from all others. In tandem, a discriminator attempts to distinguish between the joint distributions of real and generated samples. Finally, a sparsity penalty forces each generator to consider only a small subset of the variables, yielding a sparse causal graph. SAM scales easily to hundreds variables. Our experiments show the state-of-the-art performance of SAM on discovering causal structures and modeling interventions, in both acyclic and non-acyclic graphs.

Dates and versions

hal-01864239 , version 1 (29-08-2018)

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Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag. SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning. 2018. ⟨hal-01864239⟩
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