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 uplift model, extending Robinson-style decompositions to learned, vector-valued treatments. We show that the resulting estimator is expressive for policy-induced causal effects, orthogonally robust to nuisance estimation errors, and stable under small policy perturbations. Experiments on large-scale randomized platform data demonstrate improved uplift accuracy and stability in long-tailed policy regimes

翻译：本文研究组合干预下的提升值估计问题。提升值衡量干预措施（如发送优惠券或营销信息）对用户行为的纯增量因果效应，可建模为条件个体处理效应。现实中的许多干预措施具有组合特性：处理策略并非单一原子标签，而是指定了上下文依赖的动作分布。尽管近期研究开始关注结构化处理，但多数方法仍依赖分类或非透明编码，限制了模型对罕见或新部署策略的鲁棒性与泛化能力。我们提出一种将处理表示与因果语义对齐的提升估计框架：每个策略通过其在上下文-动作分量上诱导的混合分布进行表征，并通过置换不变聚合实现嵌入。该表示被整合至正交化低秩提升模型中，将罗宾逊式分解扩展至可学习的向量值处理。我们证明所得估计量能够充分表达策略诱导的因果效应，对干扰参数估计误差具有正交鲁棒性，并在微小策略扰动下保持稳定。基于大规模随机化平台数据的实验表明，本方法在长尾策略场景中显著提升了估计精度与稳定性。