提出新型平衡方法，提升多治疗场景下因果推断精度。
Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments
- 用贝里中心融合格罗莫夫-瓦瑟斯坦距离，实现全局对齐
- 计算复杂度从二次降至线性，且保持局部结构一致性
- 适合复杂多治疗场景的因果效应分析，如市场营销
在多重同时治疗的观察数据中估计异质性单个与交互治疗效应，对决策至关重要。以往研究通过成对平衡各治疗模式间的表示分布来降低估计方差，但该方法随治疗模式数量呈平方级增长，且无法保持跨模式的一致局部邻近结构，影响反事实估计效果。为此，我们提出基于新型贝里中心融合格罗莫夫-瓦瑟斯坦平衡（BFG-WB）目标的深度学习框架CIHSI-Net。BFG-WB将每个治疗模式的表示分布与一个共享的瓦瑟斯坦贝里中心对齐，实现全局对齐的同时将计算复杂度从二次降低到线性，并通过融合格罗莫夫-瓦瑟斯坦差异保留了可靠异质效应估计所需的局部邻近结构。模拟实验表明，CIHSI-Net持续优于现有先进基线；在真实营销数据上的应用也验证了其在复杂多治疗场景中的实用性。
原文摘要 · Abstract (English)
Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decision-making. To mitigate estimation variance, previous studies balance representation distributions between every pair of treatment patterns. However, such pairwise balancing scales quadratically with the number of treatment patterns and fails to preserve consistent local proximity structures across patterns, which degrades counterfactual estimation. To address these challenges, we propose the Causal Inference for Heterogeneous Single and Interaction Treatment Effects Network (CIHSI-Net), a deep learning framework built on a novel Barycentric Fused Gromov-Wasserstein Balancing (BFG-WB) objective. BFG-WB aligns the representation distribution of each treatment pattern with a shared Wasserstein barycenter, achieving global alignment while reducing the computational complexity from quadratic to linear, and its Fused Gromov-Wasserstein discrepancy preserves the local proximity structures essential for reliable heterogeneous effect estimation. Simulation studies show that CIHSI-Net consistently outperforms state-of-the-art baselines, and an application to real-world marketing data demonstrates its practical utility in complex multi-treatment scenarios.
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