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[2602.19851] Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
Summary
This paper presents a novel framework for uplift estimation in combinatorial treatments, enhancing causal effect modeling through permutation-invariant representations.
Why It Matters
Understanding uplift estimation is crucial for optimizing interventions in marketing and other fields. This framework improves the robustness and accuracy of causal effect assessments, particularly for complex, context-dependent actions, which are increasingly common in real-world applications.
Key Takeaways
- Introduces a framework for uplift estimation that aligns treatment representation with causal semantics.
- Utilizes permutation-invariant aggregation to enhance robustness and generalization.
- Demonstrates improved accuracy and stability in uplift estimation through experiments on large-scale data.
Statistics > Methodology arXiv:2602.19851 (stat) [Submitted on 23 Feb 2026] Title:Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments Authors:Xinyan Su, Jiacan Gao, Mingyuan Ma, Xiao Xu, Xinrui Wan, Tianqi Gu, Enyun Yu, Jiecheng Guo, Zhiheng Zhang View a PDF of the paper titled Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments, by Xinyan Su and Jiacan Gao and Mingyuan Ma and Xiao Xu and Xinrui Wan and Tianqi Gu and Enyun Yu and Jiecheng Guo and Zhiheng Zhang View PDF HTML (experimental) Abstract:We study uplift estimation for combinatorial treatments. Uplift measures the pure incremental causal effect of an intervention (e.g., sending a coupon or a marketing message) on user behavior, modeled as a conditional individual treatment effect. Many real-world interventions are combinatorial: a treatment is a policy that specifies context-dependent action distributions rather than a single atomic label. Although recent work considers structured treatments, most methods rely on categorical or opaque encodings, limiting robustness and generalization to rare or newly deployed policies. We propose an uplift estimation framework that aligns treatment representation with causal semantics. Each policy is represented by the mixture it induces over contextaction components and embedded via a permutation-invariant aggregation. This representation is integrated into an orthogonalized low-rank upl...