发表机构
Management Science and Engineering, Stanford University; Airbnb（斯坦福大学管理科学与工程系; 爱彼迎）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种因果数据融合方法，结合观测与实验数据，估计礼品卡计划的增量收入，发现第三方渠道和自购礼品者更具增量性。
AI 中文摘要
企业经常提供礼品卡计划以推动客户消费并提高参与度。一个核心问题是这些计划能带来多少增量收入，以及哪些渠道最有效地驱动这一增长。衡量与礼品卡计划相关的增量收入是因果推断中的一个难题，要求企业推断出如果客户从未收到礼品卡，他们本会花费多少。关于过去客户购买行为的观测数据仅在客户进行购买时才揭示其是否拥有礼品卡，因此导致客户的处置状态被系统性删失。在本文中，我们开发了一种新颖的数据融合方法以克服这一缺失数据挑战。我们通过结合来自不同人群的大型观测数据集与较小的实验数据集来识别和估计增量效应。我们的方法依赖于一个温和的可转移性条件，该条件假设礼品卡接收对购买决策的条件相对处理效应在两个人群中是不变的。我们开发了一种灵活的、基于机器学习的增量收入估计器，并建立了其渐近正态性。我们将我们的估计器应用于爱彼迎分发礼品卡的第三方和第三方渠道，发现增量效应在人群各细分部分之间存在异质性。特别是，我们不仅发现第三方渠道比第一方渠道更具增量性，而且“自购礼品者”（即可能自己购买礼品卡的客户）比更广泛的人群更具增量性。
英文摘要
Businesses regularly offer gift card programs to drive customer spending and increase engagement. A central question is how much incremental revenue these programs generate, and which channels drive it most efficiently. Measuring the incremental revenue associated with a gift card program is a challenging problem in causal inference, requiring a firm to infer how much each customer would have spent if they never received a gift card. Observational data on past customer purchasing behavior reveal possession of a gift card only when a customer makes a purchase, thus leaving a customer's treatment status systematically censored. In this paper, we develop a novel data fusion approach to overcome this missing data challenge. We identify and estimate incrementality by combining a large observational dataset with a smaller experimental dataset from a different population. Our approach relies on a mild transferability condition, which posits that the conditional relative treatment effect of gift card receipt on the decision to purchase is invariant across the two populations. We develop a flexible, machine learning-based estimator for the incremental revenue and establish its asymptotic normality. We apply our estimator across both first- and third-party channels through which Airbnb distributes gift cards, finding heterogeneity in incrementality across segments of the population. In particular, we find not only that third-party channels are more incremental than first-party ones, but also that "self-gifters" (i.e., customers likely to have purchased their own gift cards) are more incremental than the broader population.
Comments80 pages, 9 figures, 13 tables
