评估在线广告活动:大规模的因果模型

David Chan, Rong Ge, Ori Gershony, Tim Hesterberg, D. Lambert
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引用次数: 110

摘要

显示广告在网络上激增,但它们有效吗?或者它们与人们看到的所有其他广告无关?我们描述了一种快速准确地回答这些问题的方法,而不需要随机实验、调查、焦点小组或专家数据分析师。双重稳健估计可以防止观测数据中固有的选择偏差,而基于不相关结果的非参数检验提供了进一步的防御。基于现实场景的模拟表明,所得到的估计比传统的替代方法(如回归模型或倾向评分)对选择偏差的鲁棒性更强。此外,计算速度足够快,所有的处理,从数据检索到估计、测试、验证和报告生成,都在自动化的管道中进行,而不需要任何人看到原始数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating online ad campaigns in a pipeline: causal models at scale
Display ads proliferate on the web, but are they effective? Or are they irrelevant in light of all the other advertising that people see? We describe a way to answer these questions, quickly and accurately, without randomized experiments, surveys, focus groups or expert data analysts. Doubly robust estimation protects against the selection bias that is inherent in observational data, and a nonparametric test that is based on irrelevant outcomes provides further defense. Simulations based on realistic scenarios show that the resulting estimates are more robust to selection bias than traditional alternatives, such as regression modeling or propensity scoring. Moreover, computations are fast enough that all processing, from data retrieval through estimation, testing, validation and report generation, proceeds in an automated pipeline, without anyone needing to see the raw data.
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