论文标题
增量投标和归因
Incrementality Bidding and Attribution
论文作者
论文摘要
向潜在客户展示广告而不是通常称为“增量”的因果效应是广告有效性的基本问题。在数字广告中，三个主要拼图对于严格量化广告增量的核心：广告购买/竞标/定价，归因和实验。在机器学习和因果计量经济学基础的基础上，我们提出了一种方法，将这三个概念统一为竞标和归因的计算可行模型，该模型涵盖了广告效应的随机化，培训，交叉验证，评分，评分，转换和转换归因。这种方法的实施可能会确保广告回报率的重大改善。
The causal effect of showing an ad to a potential customer versus not, commonly referred to as "incrementality", is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. Building on the foundations of machine learning and causal econometrics, we propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution which spans the randomization, training, cross validation, scoring, and conversion attribution of advertising's causal effects. Implementation of this approach is likely to secure a significant improvement in the return on investment of advertising.
