AI 中文总结
以自摆乌龙假设探讨媒体测量，指出基于归因的ROAS测量及MMMs存在问题，开发基于增量性的测量模型，含受众层面随机化和PIE个体层面扩展，可解决相关测量难题。
AI 中文摘要
我们将自摆乌龙假设作为洞察媒体测量的视角。像短视频社交网络这样的需求产生（漏斗上部）广告平台能带来增量购买，但购买行为却记在下游可信市场上。基于归因的广告支出回报率（ROAS）测量中，转移的转化对源平台不可见。营销组合模型（MMMs）不知该将结果归功于哪个渠道，按周聚合渠道又缺乏预算决策所需的受众层面粒度。我们开发了一种基于增量性的测量模型，包含环境受众层面随机化（每个激活的受众都有自己的意向性治疗（ITT）实验）和实验预测增量性（PIE）的个体层面扩展，它能从个体特征学习到实验确定的增量结果映射。由于ITT对比是在渠道完整结果上计算的，估计量无偏且自摆乌龙现象消失。
英文摘要
We use the assisted own goal hypothesis as a lens into media measurement. A demand-generating (upper-funnel) advertising platform such as a short-video social network can cause an incremental purchase, yet see that purchase booked on -- and credited to -- a downstream trusted marketplace, because consumers who discover a product on the platform complete the transaction elsewhere, for example because of distrust of the generating platform as a psychological mechanism. Under attribution-based return-on-ad-spend (ROAS) measurement, the diverted conversions are invisible to the originating platform. Marketing-mix models (MMMs) do not know which channel to credit with the outcome, and channel-by-week aggregation denies the audience-level granularity that budget decisions require. We develop an incrementality-based measurement model with two ingredients: ambient audience-level randomization -- each activated audience carries its own intent-to-treat (ITT) experiment -- and an individual-level extension of Predicted Incrementality by Experimentation (PIE), which learns a mapping from individual features to experiment-identified incremental outcomes. Because ITT contrasts are computed on channel-complete outcomes, the estimator is unbiased and the own goal disappears
