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Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
Shoubo Hu · Zhitang Chen · Vahid Partovi Nia · Laiwan CHAN · Yanhui Geng

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #11

The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model. In practice, however, observations are often collected from multiple sources with heterogeneous causal models due to certain uncontrollable factors, which renders causal analysis results obtained by a single model skeptical. In this paper, we generalize the Additive Noise Model (ANM) to a mixture model, which consists of a finite number of ANMs, and provide the condition of its causal identifiability. To conduct model estimation, we propose Gaussian Process Partially Observable Model (GPPOM), and incorporate independence enforcement into it to learn latent parameter associated with each observation. Causal inference and clustering according to the underlying generating mechanisms of the mixture model are addressed in this work. Experiments on synthetic and real data demonstrate the effectiveness of our proposed approach.

Author Information

Shoubo Hu (The Chinese University of Hong Kong)
Zhitang Chen (Noah's Ark Lab,Huawei Tech. Investment Co. Ltd.)
Vahid Partovi Nia (Huawei Technologies)
Laiwan CHAN (Department of Computer Science and Engineering, Chinese University of Hong Kong)
Yanhui Geng (Huawei Montreal Research Centre)

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