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 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 remain less explored. In this work, we propose a Bayesian approach to learning causal representations from multi-environment data, focusing on the case...
| Authors | Ankur Garg, Michael Stettler, Aaron Schein et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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