[论文解读] Causal Representation Learning with Optimal Compression under Complex Treatments

Wanting Liang, Haoang Chi+1 more|arXiv (Cornell University)|Mar 12, 2026

Advanced Causal Inference Techniques被引用 0

一句话总结

论文提出了一种基于界限引导、界限优化的多治疗因果表示学习平衡方法，提出三种平衡策略（Pairwise、One-vs-All、Treatment Aggregation）以及用于选择最优压缩级别的双层方法，并扩展到 Wasserstein-测地线对照因果的扩展。

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.

研究动机与目标

• Motivate and address the bias–information trade-off in multi-treatment causal representation learning.
• Eliminate heuristic tuning of the balancing weight via a generalization bound that yields a consistent estimator for the optimal alpha.
• Propose three balancing strategies with scalable computational properties and analyze their stability as the number of treatments grows.
• Extend the framework to a generative architecture preserving Wasserstein geodesic structure for counterfactual interpolation.

提出的方法

• Derive a multi-treatment generalization bound linking factual risk and representation imbalance.
• Formulate representation learning as a constrained optimization (or penalized) problem with a balancing weight alpha.
• Introduce three balancing strategies: Pairwise (O(K^2) complexity), One-vs-All (O(K) complexity), and Treatment Aggregation via HSIC (O(1) in K).
• Define an empirical, bound-driven bilevel procedure to select alpha (BOAB: Bound-Optimized Adaptive Balancing).
• Extend to a Multi-Treatment CausalEGM architecture to preserve Wasserstein geodesic structure for counterfactual interpolation.
• Provide theoretical results on finite-sample accuracy and asymptotic normality of the alpha estimator, plus stability scaling with K.

实验结果

研究问题

• RQ1 如何在多治疗因果学习中在不依赖启发式调参的情况下，最优地平衡表示不变性和信息保留？
• RQ2 不同平衡策略（Pair、OVA、Aggregation）如何随治疗数量 K 的增长而扩展，并影响 ITE 估计的偏差与方差？
• RQ3 是否可以为基于界限的 alpha 估计量提供有限样本和渐近保证，并量化其在 K 变化时的稳定性？
• RQ4 是否存在一个 Wasserstein-测地线一致框架，使得跨治疗的反事实插值具有意义？
• RQ5 在大规模干预设置下，估计精度和效率的经验增益如何？

主要发现

• 一个多治疗泛化界限表明 ITE 误差被事实风险、表示不平衡项以及一个复杂度项共同界定。
• 最优的 alpha 可以通过一个双层程序来估计，该程序最小化基于界限的目标，避免启发式调参。
• Treatment Aggregation 在与 K 相关的平衡复杂度上达到 O(1)，缓解维数灾难。
• One-vs-All 平衡在中等 K 时表现最佳，而 Aggregation 在 K 增大时仍保持鲁棒和可扩展性（实验中演示至 K=20）。
• 在半合成和类图像数据集上的实证结果显示相对于基线的 PEHE 改善，在大规模 K 设置下 Aggregation 仍保持性能（如大规模实验）。