Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
CoRR(2024)
Abstract
Identifying latent representations or causal structures is important for good
generalization and downstream task performance. However, both fields have been
developed rather independently. We observe that several methods in both
representation and causal structure learning rely on the same data-generating
process (DGP), namely, exchangeable but not i.i.d. (independent and identically
distributed) data. We provide a unified framework, termed Identifiable
Exchangeable Mechanisms (IEM), for representation and structure learning under
the lens of exchangeability. IEM provides new insights that let us relax the
necessary conditions for causal structure identification in exchangeable
non–i.i.d. data. We also demonstrate the existence of a duality condition in
identifiable representation learning, leading to new identifiability results.
We hope this work will pave the way for further research in causal
representation learning.
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