发表机构
LinkedIn（领英）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对大规模定向投放与推荐系统的资源错配问题，提出以决策为中心的因果优化框架，经实验验证可实现主要长期价值指标7.20%的统计显著提升，证明了生产规模因果优化的可行性。
AI 中文摘要
大规模定向投放与推荐系统通常围绕预测分数构建，该分数被输入启发式方法或局部分配机制。当业务目标是增量影响时，如营销活动、激励措施和通知场景，这种范式会系统性地将资源错配给那些无论如何都会采取行动的用户。我们提出了一种以决策为中心的框架，该框架转而在全局约束下优化因果效应，在单一目标下整合三个组件：采用Transformer主干网络的因果神经网络，用于个体处理效应估计；用于感知不确定性探索的贝叶斯神经博弈层；以及基于对偶的大规模线性规划层，用于约束分配。该框架还通过Transformer编码器和结果嵌入支持序列上下文、多结果、属性条件评分。我们在公开博弈数据集上进行离线模拟、针对性架构消融实验，并在LinkedIn Feed营销流量上开展在线A/B测试对其进行评估。我们还提炼了关于因果训练数据构建、成本及投放控制的生产经验，这些对成功部署至关重要。端到端处理策略在主要长期价值指标上实现了统计显著的+7.20%提升，证明了在业务约束下生产规模因果优化的可行性。
英文摘要
Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation. When the business goal is incremental impact, as in marketing campaigns, incentives, and notifications, this paradigm systematically misallocates resources toward users who would have acted anyway. We present a decision-centric framework that instead optimizes causal effects under global constraints, aligning three components under a single objective: a causal neural network with a Transformer backbone for individual treatment-effect estimation, a Bayesian neural-bandit layer for uncertainty-aware exploration, and a dual-based large-scale linear-programming layer for constrained allocation. The framework also supports sequential context and multi-outcome, attribute-conditioned scoring through a Transformer encoder and outcome embeddings. We evaluate it with offline simulations on a public bandit dataset, targeted architectural ablations, and an online A/B test on LinkedIn Feed marketing traffic. We also distill production lessons on causal training-data construction and cost and delivery control, which were critical to successful deployment. The end-to-end treatment policy delivered a statistically significant $+7.20\%$ lift in the primary long-term-value metric, demonstrating the feasibility of production-scale causal optimization under business constraints.
