Marketplace platforms routinely evaluate pricing and allocation policies using logged observational data, yet strong offline performance does not imply that a policy is safe to deploy. In real-time bidding (RTB) marketplaces, reserve-price and floor-policy changes affect not only revenue but also fill, advertiser value, budget pacing, and competition across auctions, creating feedback and interference. The central problem is therefore not to estimate whether a policy improves an offline metric, but to determine whether the available evidence justifies direct launch or only further validation. In this regard, we propose a support-aware decision-support system (DSS) that distinguishes promising from actionable evidence. The framework integrates replay, support-aware off-policy evaluation (OPE), conservative lower-bound ranking, multi-sided guardrails, out-of-time validation, sensitivity analysis, and interference-aware validation design into a claim-preserving pipeline that outputs a launch-readiness classification rather than a single performance estimate. Applying the framework to iPinYou-style RTB logs, we identify a margin-gated floor policy as the leading candidate, with a 47.7% replay yield lift, a 45.8% conservative lower-tail lift, and stable out-of-time performance. However, the framework does not recommend direct launch. A decision-rule ablation shows that simplified pipelines select the same policy but incorrectly recommend deployment, leaving key causal assumptions unresolved. In contrast, the proposed DSS selects the same policy but changes the action to online validation, reflecting missing evidence on propensities, bidder response, and interference. Overall, the contribution is a reproducible DSS protocol that prevents decision overclaim under partial identification and converts offline evaluation into an auditable, action-oriented recommendation.

翻译：市场平台通常利用记录的观测数据评估定价和分配策略，但强劲的离线性能并不意味该策略可安全部署。在实时竞价（RTB）市场中，保留价与底价策略的变动不仅影响收入，还会波及填充率、广告主价值、预算节奏及跨竞拍竞争，产生反馈与干扰效应。因此，核心问题并非评估策略是否提升离线指标，而是判定现有证据足以支持直接发布，抑或仅能支撑进一步验证。基于此，我们提出一种觉察支持力的决策支持系统（DSS），能够区分有前景证据与可行动证据。该框架将回放、支持感知型离线策略评估（OPE）、保守下限排序、多边防护栏、时外验证、敏感性分析及干扰感知验证设计整合为一条遵循声明保留的流水线，最终输出发布准备就绪分类而非单一性能估计。在iPinYou风格RTB日志上的应用表明，边际门控底价策略以47.7%的回放收益提升率、45.8%的保守下限提升率及稳定的时外性能成为首要候选策略。然而，该框架并不推荐直接发布。决策规则消融实验显示，简化流水线虽选中相同策略，但错误建议部署，遗留关键因果假设未解。相比之下，所提出的DSS虽选择同一策略，但将行动转变为在线验证，反映出关于倾向性、竞标者响应及干扰的证据缺失。总体而言，本研究的贡献在于提出一个可复现的DSS协议，可在部分识别条件下防止决策过度声明，将离线评估转化为可审计、面向行动的建议。