3 papers
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…
cs.LG2025
Transferring Causal Effects using Proxies
Manuel Iglesias-Alonso, Felix Schur, Julius von Kügelgen +1
We consider the problem of estimating a causal effect in a multi-domain setting. The causal effect of interest is confounded by an unobserved confounder and can change between the…