[2508.04492] Learning Robust Intervention Representations with Delta Embeddings

Computer Science > Computer Vision and Pattern Recognition arXiv:2508.04492 (cs) [Submitted on 6 Aug 2025 (v1), last revised 28 Feb 2026 (this version, v3)] Title:Learning Robust Intervention Representations with Delta Embeddings Authors:Panagiotis Alimisis, Christos Diou View a PDF of the paper titled Learning Robust Intervention Representations with Delta Embeddings, by Panagiotis Alimisis and 1 other authors View PDF HTML (experimental) Abstract:Causal representation learning has attracted significant research interest during the past few years, as a means for improving model generalization and robustness. Causal representations of interventional image pairs (also called ``actionable counterfactuals'' in the literature), have the property that only variables corresponding to scene elements affected by the intervention / action are changed between the start state and the end state. While most work in this area has focused on identifying and representing the variables of the scene under a causal model, fewer efforts have focused on representations of the interventions themselves. In this work, we show that an effective strategy for improving out of distribution (OOD) robustness is to focus on the representation of actionable counterfactuals in the latent space. Specifically, we propose that an intervention can be represented by a Causal Delta Embedding that is invariant to the visual scene and sparse in terms of the causal variables it affects. Leveraging this insight, we...