Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michele Sebag

Abstract:

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
between different 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 enforce the
optimization of the graph structure and parameters through stochastic gradient descent. SAM is
extensively experimentally validated on synthetic and real data