用对抗学习发现带相关噪声的线性因果结构
Causal discovery for linear causal model with correlated noise: an Adversarial Learning Approach
- 基于f-GAN框架，将因果结构学习转化为对抗优化问题
- 通过最小化真实数据与生成数据间的f散度实现结构推断
- 适用于存在未观测混杂因素的复杂因果推断场景
从存在未测量混杂因素的数据中进行因果发现是一个具有挑战性的问题。本文提出一种基于f-GAN框架的方法，可学习二元因果结构且不依赖具体权重值。我们将结构学习问题重新表述为最小化贝叶斯自由能，并证明该问题等价于最小化真实数据分布与模型生成分布之间的f散度。利用f-GAN框架，将该目标转化为一个极小极大对抗优化问题。我们采用Gumbel-Softmax松弛方法在离散图空间中执行梯度搜索。
原文摘要 · Abstract (English)
Causal discovery from data with unmeasured confounding factors is a challenging problem. This paper proposes an approach based on the f-GAN framework, learning the binary causal structure independent of specific weight values. We reformulate the structure learning problem as minimizing Bayesian free energy and prove that this problem is equivalent to minimizing the f-divergence between the true data distribution and the model-generated distribution. Using the f-GAN framework, we transform this objective into a min-max adversarial optimization problem. We implement the gradient search in the discrete graph space using Gumbel-Softmax relaxation.
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