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

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...