Drton group
Unpaired Multi-Domain Causal Representation Learning
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, only the marginal distribution in each domain is observed but not their joint distribution. In this talk, I will present conditions for identifiability of the joint distribution and the shared causal graph in a linear setup.