Causal Representation Learning with Optimal Compression under Complex Treatments

• URL: http://arxiv.org/abs/2603.11907v1
• Date: Thu, 12 Mar 2026 13:20:18 GMT
• Title: Causal Representation Learning with Optimal Compression under Complex Treatments
• Abstract summary: This paper derives a novel multi-treatment generalization bound and proposes a theoretical estimator for the optimal balancing weight.<n>Our approach significantly outperforms traditional models in estimation accuracy and efficiency, particularly in large-scale intervention scenarios.
• Score: 10.945117945979566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating Individual Treatment Effects (ITE) in multi-treatment scenarios faces two critical challenges: the Hyperparameter Selection Dilemma for balancing weights and the Curse of Dimensionality in computational scalability. This paper derives a novel multi-treatment generalization bound and proposes a theoretical estimator for the optimal balancing weight $α$, eliminating expensive heuristic tuning. We investigate three balancing strategies: Pairwise, One-vs-All (OVA), and Treatment Aggregation. While OVA achieves superior precision in low-dimensional settings, our proposed Treatment Aggregation ensures both accuracy and O(1) scalability as the treatment space expands. Furthermore, we extend our framework to a generative architecture, Multi-Treatment CausalEGM, which preserves the Wasserstein geodesic structure of the treatment manifold. Experiments on semi-synthetic and image datasets demonstrate that our approach significantly outperforms traditional models in estimation accuracy and efficiency, particularly in large-scale intervention scenarios.

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