基于变分推断的联合因果结构与聚类发现
Joint Causal Structure and Cluster Discovery Using Variational Inference
- Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）
- RIKEN Center for AI Project（理化学研究所人工智能项目中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于变分推断的新方法，可同时推断潜在聚类与因果结构，经合成及真实数据集验证了其在聚类与因果发现上的有效性。
AI中文摘要：
因果发现旨在理解单个随机变量之间的关系。在脑成像、气候建模等诸多应用中，考虑变量组之间的交互作用更具意义。现有方法在建模交互时假定这类组或聚类的知识是明确可得的，但实际中这些聚类及其之间的因果关系都是潜在的。本文提出一种基于变分推断的新方法，用于同时推断潜在聚类与因果结构。我们分别基于分类模型和伯努利模型构建变分分布，以学习关于聚类和图结构的近似后验；推导变分下界及估计技术，用于学习变分参数与模型参数。在合成数据集和真实数据集上，我们的方法在聚类与因果发现方面的有效性得到了验证。
英文摘要：
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.
