可识别性
联合概率分布
代表(政治)
领域(数学分析)
边际分布
图形
潜变量
计算机科学
数学
分布(数学)
理论计算机科学
人工智能
机器学习
统计
数学分析
政治
随机变量
政治学
法学
作者
Nils Sturma,Chandler Squires,Mathias Drton,Caroline Uhler
出处
期刊:Cornell University - arXiv
日期:2023-01-01
标识
DOI:10.48550/arxiv.2302.00993
摘要
The goal of causal representation learning is to find a representation of data that consists of causally related latent variables. We consider a setup where one has access to data from multiple domains that potentially share a causal representation. Crucially, observations in different domains are assumed to be unpaired, that is, we only observe the marginal distribution in each domain but not their joint distribution. In this paper, we give sufficient conditions for identifiability of the joint distribution and the shared causal graph in a linear setup. Identifiability holds if we can uniquely recover the joint distribution and the shared causal representation from the marginal distributions in each domain. We transform our identifiability results into a practical method to recover the shared latent causal graph.
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