Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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

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.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01864239
Contributor : Diviyan Kalainathan <>
Submitted on : Wednesday, August 29, 2018 - 3:23:38 PM
Last modification on : Wednesday, September 16, 2020 - 5:51:26 PM

Links full text

Identifiers

  • HAL Id : hal-01864239, version 1
  • ARXIV : 1803.04929

Citation

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

Share

Metrics

Record views

214