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Disentangling Observed Causal Effects from Latent Confounders using Method of Moments

Liu, Anqi and Liu, Hao and Li, Tongxin and Karimi-Bidhendi, Saeed and Yue, Yisong and Anandkumar, Anima (2021) Disentangling Observed Causal Effects from Latent Confounders using Method of Moments. . (Unpublished)

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Discovering the complete set of causal relations among a group of variables is a challenging unsupervised learning problem. Often, this challenge is compounded by the fact that there are latent or hidden confounders. When only observational data is available, the problem is ill-posed, i.e. the causal relationships are non-identifiable unless strong modeling assumptions are made. When interventions are available, we provide guarantees on identifiability and learnability under mild assumptions. We assume a linear structural equation model (SEM) with independent latent factors and directed acyclic graph (DAG) relationships among the observables. Since the latent variable inference is based on independent component analysis (ICA), we call this model SEM-ICA. We use the method of moments principle to establish model identifiability. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions. Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Liu, Hao0000-0002-7405-1578
Li, Tongxin0000-0002-9806-8964
Yue, Yisong0000-0001-9127-1989
Record Number:CaltechAUTHORS:20210225-132714927
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108203
Deposited By: George Porter
Deposited On:26 Feb 2021 15:32
Last Modified:26 Feb 2021 15:32

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