Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models

Autor:	Hu, Shoubo, Chen, Zhitang, Nia, Vahid Partovi, Chan, Laiwan, Geng, Yanhui
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published at NIPS 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.08568 Zobrazit plný text záznamu View this record from Arxiv