Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
潜在因子诱导的节点簇之间的因果结构发现AI 解读 ↓
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潜在因子诱导的节点簇之间的因果结构发现
- 研究问题
- 存在潜变量时，能否识别共享潜在父节点的观测变量簇，并恢复簇间联系及包含观测节点与潜节点的因果图结构？
- 方法与思路
- 作者限制潜变量因果 DAG 的结构，定义潜在因子因果模型，设计依次发现变量簇、簇的偏序和完整结构的三阶段算法。
- 材料陈述的贡献
- 摘要称建立了模型可识别性与算法一致性，并在合成及蛋白质质谱半合成数据中较好恢复变量簇及部分潜因子相关边。
- 阅读关注点
- 应核对模型结构限制、可识别性与一致性的具体条件，并区分变量簇、簇排序和因果边恢复各自所需的证据。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列完整识别条件，簇恢复表现不能直接等同于完整因果图恢复。
原文摘要
We consider the problem of learning the structure of a causal directed acyclic graph (DAG) model in the presence of latent variables. We define latent factor causal models (LFCMs) as a restriction on causal DAG models with latent variables, which are composed of clusters of observed variables that share the same latent parent and connections between these clusters given by edges pointing from the observed variables to latent variables. LFCMs are motivated by gene regulatory networks, where regulatory edges, corresponding to transcription factors, connect spatially clustered genes. We show identifiability results on this model and design a consistent three-stage algorithm that discovers clusters of observed nodes, a partial ordering over clusters, and finally, the entire structure over both observed and latent nodes. We evaluate our method in a synthetic setting, demonstrating its ability to almost perfectly recover the ground truth clustering even at relatively low sample sizes, as well as the ability to recover a significant number of the edges from observed variables to latent factors. Finally, we apply our method in a semi-synthetic setting to protein mass spectrometry data with a known ground truth network, and achieve almost perfect recovery of the ground truth variable clusters.