打破决定论：用于广告拍卖中可靠非策略评估的随机建模
Breaking Determinism: Stochastic Modeling for Reliable Off-Policy Evaluation in Ad Auctions
AI总结：
本文提出了一种用于确定性拍卖环境的非策略评估框架，通过重新利用投标景观模型近似倾向分数，实现了高效且可靠的反事实评估，显著提升了CTR预测的准确性。
摘要：
在线A/B测试是评估新广告政策的黄金标准，消耗大量工程资源并可能造成显著的收入损失，因为部署表现不佳的变体。这促使使用非策略评估（OPE）进行快速离线评估。然而，将OPE应用于广告拍卖比在推荐系统等常见随机策略的领域更具挑战性。在在线广告拍卖中，最高出价的广告通常赢得展示，导致一种确定性的“赢家通吃”设置。这使得非获胜广告的曝光概率为零，使标准OPE估计器无法应用。我们引入了第一个针对确定性拍卖的OPE原理框架，通过重新利用投标景观模型来近似倾向分数。该模型使我们能够推导出稳健的近似倾向分数，从而能够使用稳定的估计器如Self-Normalized Inverse Propensity Scoring（SNIPS）进行反事实评估。我们在AuctionNet模拟基准和一个大型工业平台的2周在线A/B测试中验证了我们的方法。我们的方法在在线结果中表现出显著的契合度，在CTR预测中实现了92%的平均方向准确性（MDA），显著优于参数基线。MDA是指导部署决策最关键的指标，因为它反映了正确预测新模型是否会提高或损害性能的能力。本工作贡献了第一个实用且经过验证的框架，用于可靠地在确定性拍卖环境中进行OPE，提供了一种高效替代昂贵且有风险的在线实验的方法。
英文摘要：
Online A/B testing, the gold standard for evaluating new advertising policies, consumes substantial engineering resources and risks significant revenue loss from deploying underperforming variations. This motivates the use of Off-Policy Evaluation (OPE) for rapid, offline assessment. However, applying OPE to ad auctions is fundamentally more challenging than in domains like recommender systems, where stochastic policies are common. In online ad auctions, it is common for the highest-bidding ad to win the impression, resulting in a deterministic, winner-takes-all setting. This results in zero probability of exposure for non-winning ads, rendering standard OPE estimators inapplicable. We introduce the first principled framework for OPE in deterministic auctions by repurposing the bid landscape model to approximate the propensity score. This model allows us to derive robust approximate propensity scores, enabling the use of stable estimators like Self-Normalized Inverse Propensity Scoring (SNIPS) for counterfactual evaluation. We validate our approach on the AuctionNet simulation benchmark and against 2-weeks online A/B test from a large-scale industrial platform. Our method shows remarkable alignment with online results, achieving a 92\% Mean Directional Accuracy (MDA) in CTR prediction, significantly outperforming the parametric baseline. MDA is the most critical metric for guiding deployment decisions, as it reflects the ability to correctly predict whether a new model will improve or harm performance. This work contributes the first practical and validated framework for reliable OPE in deterministic auction environments, offering an efficient alternative to costly and risky online experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
