Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations
Causal representation learning (CRL) aims to learn low-dimensional causal latent variables from high-dimensional observations. While identifiability has been extensively studied for CRL, estimation has been less explored. In this paper, we explore the use of empirical Bayes (EB) to estimate causal representations. In particular, we consider the problem of learning from data from multiple domains, where differences between domains are modeled by interventions in a shared underlying causal model. Multi-domain CRL naturally poses a simultaneous inference problem that EB is designed to tackle. Here, we propose an EB $f$-modeling algorithm that improves the quality of learned causal variables ...
| Authors | Bohan Wu, Julius von Kügelgen, David M. Blei |
|---|---|
| Primary category | stat.ML |
| Published | 19 March 2026 |
| Last updated at source | 21 March 2026 |
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