Online Internet platforms require sophisticated marketing strategies to optimize user retention and platform revenue -- a classical resource allocation problem. Traditional solutions adopt a two-stage pipeline: machine learning (ML) for predicting individual treatment effects to marketing actions, followed by operations research (OR) optimization for decision-making. This paradigm presents two fundamental technical challenges. First, the prediction-decision misalignment: Conventional ML methods focus solely on prediction accuracy without considering downstream optimization objectives, leading to improved predictive metrics that fail to translate to better decisions. Second, the bias-variance dilemma: Observational data suffers from multiple biases (e.g., selection bias, position bias), while experimental data (e.g., randomized controlled trials), though unbiased, is typically scarce and costly -- resulting in high-variance estimates. We propose Bi-level Decision-Focused Causal Learning (Bi-DFCL) that systematically addresses these challenges. First, we develop an unbiased estimator of OR decision quality using experimental data, which guides ML model training through surrogate loss functions that bridge discrete optimization gradients. Second, we establish a bi-level optimization framework that jointly leverages observational and experimental data, solved via implicit differentiation. This novel formulation enables our unbiased OR estimator to correct learning directions from biased observational data, achieving optimal bias-variance tradeoff. Extensive evaluations on public benchmarks, industrial marketing datasets, and large-scale online A/B tests demonstrate the effectiveness of Bi-DFCL, showing statistically significant improvements over state-of-the-art. Currently, Bi-DFCL has been deployed at Meituan, one of the largest online food delivery platforms in the world.

翻译：在线互联网平台需要复杂的营销策略来优化用户留存和平台收入——这是一个经典的资源分配问题。传统解决方案采用两阶段流程：首先利用机器学习（ML）预测营销行为对个体的处理效应，随后通过运筹学（OR）优化进行决策。该范式存在两个根本性技术挑战。其一，预测与决策的错位：传统ML方法仅关注预测精度而未考虑下游优化目标，导致改进的预测指标无法转化为更优决策。其二，偏差-方差困境：观测数据存在多重偏差（如选择偏差、位置偏差），而实验数据（如随机对照试验）虽无偏差但通常稀缺且成本高昂——导致估计方差过高。我们提出双层决策导向因果学习（Bi-DFCL）系统性地解决这些挑战。首先，我们利用实验数据构建OR决策质量的无偏估计量，通过连接离散优化梯度的代理损失函数指导ML模型训练。其次，我们建立双层优化框架，通过隐函数微分联合利用观测与实验数据。这一新颖的公式化方法使我们能够利用无偏OR估计量校正来自有偏观测数据的学习方向，实现最优的偏差-方差权衡。在公开基准、工业营销数据集及大规模在线A/B测试上的广泛评估证明了Bi-DFCL的有效性，其表现较现有最优方法具有统计显著提升。目前，Bi-DFCL已在全球最大在线外卖平台之一美团完成部署。