同时发现变量隐含分组与因果结构，无需预先知道分组信息。
Joint Causal Structure and Cluster Discovery Using Variational Inference
- 用变分推断联合学习变量分组和因果图结构
- 在合成与真实数据上均验证了分组与因果发现的有效性
- 适合处理分组未知的复杂系统分析，如脑成像、气候建模
因果发现旨在理解单个随机变量之间的关系。在脑成像、气候建模等应用中，关注变量组之间的交互更为重要。现有方法通常假设分组信息已知，但实际中这些分组及其间的因果关系往往是隐含的。本文提出一种基于变分推断的新方法，可同时推断隐含分组与因果结构。通过使用类别分布和伯努利分布分别构建分组和图结构的变分近似后验，推导出变分下界及参数估计方法。在合成数据和真实数据集上的实验表明，该方法能有效实现分组与因果结构的联合发现。
原文摘要 · Abstract (English)
Causal discovery aims to understand the relationships between individual random variables. In many applications, such as brain imaging and climate modeling, it is more meaningful to consider interactions among groups of variables. Existing methods assume that knowledge of such groups or clusters is explicitly available when modeling interactions. However, in practice, these clusters as well as the causal relationships among them, are latent. In this paper, we present a novel approach based on variational inference to simultaneously infer both the latent clusters and causal structures. We learn an approximate posterior over clusters and graph-structure by considering variational distributions based on categorical and Bernoulli models respectively. We derive variational lower bounds and estimation techniques to learn variational and model parameters. The effectiveness of our proposed methods for cluster and causal discovery are demonstrated on both synthetic and real data sets.
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