Measuring Gift Card Program Incrementality via Causal Data Fusion
通过因果数据融合衡量礼品卡项目的增量收入AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究礼品卡处理的反事实增量收入，并通过实验与观察数据融合进行因果识别。
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通过因果数据融合衡量礼品卡项目的增量收入
- 研究问题
- 当历史购买记录仅在购买发生时揭示礼品卡持有状态，如何识别礼品卡带来的增量收入，并比较不同分发渠道与人群？
- 方法与思路
- 作者融合大规模观察数据与来自另一人群的小规模实验数据，假设购买决策的条件相对处理效应可迁移，并构造机器学习估计量。
- 材料陈述的贡献
- 摘要称实现增量收入的识别估计并建立渐近正态性；Airbnb 应用发现渠道与人群异质性，包括第三方及自购群体增量更高。
- 阅读关注点
- 应核对条件相对效应的定义、跨人群可迁移性与数据重叠要求、处理状态删失的处理，以及收入估计和推断所需条件。
- 阅读边界
- 依据 arXiv v1 预印本摘要，不能声称已同行评审或期刊接收。可迁移性虽被作者称为温和，仍需核对；应用数值与完整条件未提供。
原文摘要
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.