提出新方法识别不同环境下因果方向并聚类因果机制。
Hybrid Causal Identification and Causal Mechanism Clustering
- 用混合变分自编码器建模多种因果机制。
- 在模拟与真实数据上优于现有最先进方法。
- 适合处理多环境、异质性因果关系的数据分析。
双向因果方向识别是因果推断中的基础且关键问题。现有基于加性噪声的二元因果方法通常仅依赖单一因果机制构建模型。现实中观测数据常来自不同环境，具有异质性因果关系。为此，本文提出混合条件变分因果推断模型（MCVCI），用于推断异质因果关系。基于混合加性噪声模型（HANM）的可识别性，MCVCI结合高斯混合模型与神经网络的优异拟合能力，巧妙利用混合条件变分自编码器的概率边界似然作为因果决策准则。此外，将因果异质性建模为聚类数量，提出混合条件变分因果聚类（MCVCC）方法，可揭示因果机制表达。相较于最先进方法，该文提出的模型在多个模拟与真实数据集上均取得综合最优性能，验证了其有效性。
原文摘要 · Abstract (English)
Bivariate causal direction identification is a fundamental and vital problem in the causal inference field. Among binary causal methods, most methods based on additive noise only use one single causal mechanism to construct a causal model. In the real world, observations are always collected in different environments with heterogeneous causal relationships. Therefore, on observation data, this paper proposes a Mixture Conditional Variational Causal Inference model (MCVCI) to infer heterogeneous causality. Specifically, according to the identifiability of the Hybrid Additive Noise Model (HANM), MCVCI combines the superior fitting capabilities of the Gaussian mixture model and the neural network and elegantly uses the likelihoods obtained from the probabilistic bounds of the mixture conditional variational auto-encoder as causal decision criteria. Moreover, we model the casual heterogeneity into cluster numbers and propose the Mixture Conditional Variational Causal Clustering (MCVCC) method, which can reveal causal mechanism expression. Compared with state-of-the-art methods, the comprehensive best performance demonstrates the effectiveness of the methods proposed in this paper on several simulated and real data.
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