Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

• URL: http://arxiv.org/abs/2606.27114v1
• Date: Thu, 25 Jun 2026 14:49:50 GMT
• Title: Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
• Abstract summary: Uplift modeling, crucial for estimating individual treatment effects, faces dual challenges.<n>We propose the Cross-Head Attention Uplift Network (CHAUN) and Robust Adrialversa Inverse Propensity Score (RA-IPS)<n> RA-IPS enhances robustness, outperforming standard IPS by 5.4% under unobserved confounding.
• Score: 22.76545279913699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios. In this paper, we propose the Cross-Head Attention Uplift Network (CHAUN) and Robust Adversarial Inverse Propensity Score (RA-IPS) method to address these limitations. CHAUN employs shared feature embeddings and cross-head attention mechanisms to dynamically integrate treatment-specific and control-specific representations, enhancing inter-group correlation modeling. Theoretically, we prove that access to the true propensity scores ensures ITE identifiability even with unobserved confounders. For practical scenarios lacking true propensity scores, RA-IPS adversarially optimizes propensity weights within constrained uncertainty sets to mitigate bias from unobserved variables. Experiments on public datasets (CRITEO-UPLIFT, LAZADA) and a production e-commerce dataset demonstrate CHAUN's superiority over state-of-the-art uplift models, achieving relative improvements of up to 25.6% in QINI scores. RA-IPS further enhances robustness, outperforming standard IPS by 5.4% under unobserved confounding. The results validate the effectiveness of our proposed methods in real-world causal inference tasks.

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