Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation

• URL: http://arxiv.org/abs/2606.13821v1
• Date: Thu, 11 Jun 2026 18:53:49 GMT
• Title: Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation
• Abstract summary: We propose a general framework for IPTB estimation with ordinal outcomes under discrete dose assignments.<n>Our approach recasts the problem as binary classification over the unobserved sign of the individual treatment effect.<n>We demonstrate that attention-based aggregation consistently outperforms kernel alternatives.
• Score: 0.5872014229110213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the probability that a treatment outperforms a control for an individual patient, called the Individual Probability of Treatment Benefit (IPTB), offers a clinically intuitive alternative to population-average metrics. However, existing methods for IPTB estimation are largely confined to binary treatment settings, despite the prevalence of dose-varying interventions in clinical practice. We propose a general framework for IPTB estimation with ordinal outcomes under discrete dose assignments, called Dose-AIPTB (Dose Attention-based IPTB). Our approach recasts the problem as binary classification over the unobserved sign of the individual treatment effect, constructing pseudo-labels from covariate-similar pairwise comparisons and aggregating them via attention mechanisms or Nadaraya-Watson kernel regression. This formulation naturally accommodates multiple discrete dose levels, extending beyond the binary treatment paradigm. Through numerical experiments on real-world and synthetic data under covariate shift, varying sample sizes, and heterogeneous outcomes, we demonstrate that attention-based aggregation consistently outperforms kernel alternatives. The framework provides a foundation for personalized dose selection grounded in individual-level benefit probabilities. Codes implementing the model are publicly available at https://github.com/NTAILab/AIPTBDose.

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