Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising

• 成果类型: Article
• 署名作者: Singal, Raghav; Besbes, Omar; Desir, Antoine; Goyal, Vineet; Iyengar, Garud
• 署名单位: Dartmouth College; Columbia University; INSEAD Business School; Columbia University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4263
• 发表日期: 2022
• 页码: 7457-7479
• 关键词: digital economy; Online advertising; attribution; Markov chain; Shapley value; causality

摘要

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions such as emails, display ads, and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, most do not have any formal justification. The main contribution in this work is to propose an axiomatic framework for attribution in online advertising. We show that the most common heuristics can be cast under the framework and illustrate how these may fail. We propose a novel attribution metric, which we refer to as counterfactual adjusted Shapley value (CASV), which inherits the desirable properties of the traditional Shapley value while overcoming its shortcomings in the online advertising context. We also propose a Markovian model for the user journey through the conversion funnel, in which ad actions may have disparate impacts at different stages. We use the Markovian model to compare our metric with commonly used metrics. Furthermore, under the Markovian model, we establish that the CASV metric coincides with an adjusted unique-uniform attribution scheme. This scheme is efficiently implementable and can be interpreted as a correction to the commonly used uniform attribution scheme. We supplement our theoretical developments with numerical experiments using a real-world large-scale data set.