A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries in the data, 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 overall joint conditional distribution, and that of the original data. A learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the end-to-end optimization of the graph structure and parameters through stochastic gradient descent. Besides a theoretical analysis of the approach in the large sample limit, SAM is extensively experimentally validated on synthetic and real data.

翻译：本文介绍了一种新的因果发现方法,即 " 结构性统计模型(SAM) " 。利用数据中有条件的不依赖性和分布不对称两者的杠杆作用,SAM旨在从观测数据中寻找潜在的因果结构。这种方法基于不同玩家之间的游戏,以有条件地将每种变量分布作为神经网,以及旨在区别总体联合有条件分布和原始数据分布的对立方。使用将分布估计、宽度和周期性制约相结合的学习标准,通过随机梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度来实施图形结构和参数的端到端优化。除了对大样本极限方法的理论分析外,SAM还广泛对合成数据和真实数据进行了实验性验证。