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.

本研究提出了一种新的因果发现方法 Structural Agnostic Modeling（SAM），该方法结合了条件独立性和分布不对称性，并旨在从观察数据中找到潜在的因果结构。研究使用神经网络作为不同参与者之间的游戏，并通过优化图结构和参数来实现优化学习准则。该方法通过在合成和真实数据上进行广泛的实验验证。