Cross-Treatment Effect Estimation for Multi-Category, Multi-Valued Causal Inference via Dynamic Neural Masking

• URL: http://arxiv.org/abs/2511.01641v1
• Date: Mon, 03 Nov 2025 14:50:02 GMT
• Title: Cross-Treatment Effect Estimation for Multi-Category, Multi-Valued Causal Inference via Dynamic Neural Masking
• Authors: Xiaopeng Ke, Yihan Yu, Ruyue Zhang, Zhishuo Zhou, Fangzhou Shi, Chang Men, Zhengdan Zhu,
• Abstract summary: XTNet is a novel network architecture for multi-category, multi-valued treatment effect estimation.<n>XTNet consistently outperforms state-of-the-art baselines in both ranking accuracy and effect estimation quality.
• Score: 3.9483106794081184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual causal inference faces significant challenges when extended to multi-category, multi-valued treatments, where complex cross-effects between heterogeneous interventions are difficult to model. Existing methodologies remain constrained to binary or single-type treatments and suffer from restrictive assumptions, limited scalability, and inadequate evaluation frameworks for complex intervention scenarios. We present XTNet, a novel network architecture for multi-category, multi-valued treatment effect estimation. Our approach introduces a cross-effect estimation module with dynamic masking mechanisms to capture treatment interactions without restrictive structural assumptions. The architecture employs a decomposition strategy separating basic effects from cross-treatment interactions, enabling efficient modeling of combinatorial treatment spaces. We also propose MCMV-AUCC, a suitable evaluation metric that accounts for treatment costs and interaction effects. Extensive experiments on synthetic and real-world datasets demonstrate that XTNet consistently outperforms state-of-the-art baselines in both ranking accuracy and effect estimation quality. The results of the real-world A/B test further confirm its effectiveness.

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