发表机构
NTT DOCOMO, INC.; Yokohama City University（NTT都科摩公司; 横滨市立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多处理因果推断中现有成对平衡方法的复杂度高、局部结构保留差的问题，本文提出CIHSI-Net框架，基于BFG-WB目标实现高效全局对齐与局部结构保留，模拟及真实数据应用验证了其性能优势。
AI 中文摘要
从观测数据中估计多种同时处理下的异质性单一及交互处理效应对决策至关重要。为降低估计方差，过往研究在每对处理模式间平衡表示分布，但这种成对平衡的计算复杂度随处理模式数量呈二次增长，且无法在各模式间保留一致的局部邻近结构，从而降低反事实估计的性能。为解决这些挑战，我们提出了异质性单一及交互处理效应因果推断网络（CIHSI-Net），这是一个基于新型重心融合格罗莫夫-瓦瑟斯坦平衡（BFG-WB）目标的深度学习框架。BFG-WB将各处理模式的表示分布与共享的瓦瑟斯坦重心对齐，实现全局对齐的同时将计算复杂度从二次降至线性，其融合格罗莫夫-瓦瑟斯坦距离则保留了对可靠异质性效应估计至关重要的局部邻近结构。模拟研究表明，CIHSI-Net始终优于最先进的基线方法，将其应用于真实世界营销数据则证明了其在复杂多处理场景中的实用价值。
英文摘要
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.
Comments26 pages
