提出新方法提升组合干预的因果效应评估精度与稳定性
Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
- 用置换不变聚合表示策略，捕捉其在不同情境下的行为分布
- 结合正交化低秩模型，在长尾策略场景下提升预测准确率和稳定性
- 适合大规模平台中复杂、多变的个性化干预评估任务
我们研究组合干预下的 uplift 估计问题。Uplift 衡量干预（如发送优惠券或营销信息）对用户行为的纯增量影响，建模为条件个体处理效应。许多现实干预具有组合性：处理是基于上下文的行动分布策略，而非单一原子标签。尽管近期工作考虑了结构化处理，但多数方法依赖分类或不透明编码，限制了对罕见或新部署策略的鲁棒性和泛化能力。本文提出一种对齐因果语义的 uplift 估计框架。每个策略通过其在上下文-动作组件上诱导的混合分布进行表征，并采用置换不变聚合进行嵌入。该表征集成进正交化低秩 uplift 模型，将 Robinson 风格分解扩展至学习得到的向量值处理。我们证明该估计器对策略诱导的因果效应具有表达力，对扰动估计误差具有正交鲁棒性，并在小策略扰动下保持稳定。在大规模随机平台数据上的实验表明，该方法在长尾策略环境下显著提升了 uplift 准确性和稳定性。
原文摘要 · Abstract (English)
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
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