Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applications

arXiv:2606.06288v1 Announce Type: cross Abstract: Causal representation learning aims to infer the high-level latent causal concepts that give rise to observed low-level measurements. This is particularly relevant for heterogeneous data from different environments or domains since distribution shifts often arise through sparse, localized changes in some of the underlying causal mechanisms, while other parts of the generative process remain unchanged. Whereas identifiability of causal representations has been studied extensively, practical uncertainty-aware methods and real-world use cases rema

Source: arXiv cs.LG — read the full report at the original publisher.

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