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.

翻译：在多干预场景中估计个体处理效应面临两大关键挑战：平衡权重的超参数选择困境与计算可扩展性的维度灾难。本文推导了一种新颖的多干预泛化界，并提出了最优平衡权重$α$的理论估计器，从而消除了昂贵的启发式调参。我们研究了三种平衡策略：成对平衡、一对多平衡与干预聚合平衡。尽管一对多方法在低维场景中实现了更优的精度，我们提出的干预聚合方法在干预空间扩展时既能保证准确性，又能维持O(1)级别的可扩展性。此外，我们将该框架扩展为生成式架构——多干预因果嵌入生成模型，该模型保持了干预流形的Wasserstein测地线结构。在半合成与图像数据集上的实验表明，我们的方法在估计精度与效率方面显著优于传统模型，尤其在大规模干预场景中表现突出。