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stat.ML2026
Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applications
Ankur Garg, Michael Stettler, Aaron Schein +1
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 heterogeneou…
stat.ML2026
Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations
Bohan Wu, Julius von Kügelgen, David M. Blei
Causal representation learning (CRL) aims to learn low-dimensional causal latent variables from high-dimensional observations. While identifiability has been extensively studied fo…