Problem
Research questions and friction points this paper is trying to address.
causal discovery
latent clusters
variable interactions
Joint Causal Structure and Cluster Discovery Using Variational Inference
๐ค AI Summary
ๆฌๆๆๅบไธ็งๅบไบๅๅๆจๆญ็ๆฐๆนๆณ๏ผๅๆถๆจๆญๆฝๅจ็ๅ้็ฐๅๅ ๆ็ปๆ๏ผ่งฃๅณไบๅจไธ็ฅ้ๅ้็ฐๆ
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๐ Abstract
Causal discovery aims to understand the relationships between individual random variables. In many applications, such as brain imaging and climate modeling, it is more meaningful to consider interactions among groups of variables. Existing methods assume that knowledge of such groups or clusters is explicitly available when modeling interactions. However, in practice, these clusters as well as the causal relationships among them, are latent. In this paper, we present a novel approach based on variational inference to simultaneously infer both the latent clusters and causal structures. We learn an approximate posterior over clusters and graph-structure by considering variational distributions based on categorical and Bernoulli models respectively. We derive variational lower bounds and estimation techniques to learn variational and model parameters. The effectiveness of our proposed methods for cluster and causal discovery are demonstrated on both synthetic and real data sets.
Innovation
Methods, ideas, or system contributions that make the work stand out.
variational inference
latent clusters
causal structures
variational distributions
approximate posterior
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