2023/12/16 by Yijun Li, Cheuk Hang Leung, Li, Yijun +15
Business, Management and Accounting · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Economics and business #Financial Literacy, Pension, Retirement Analysis #General Finance (q-fin.GN) #Methodology (stat.ME) #Retirement, Disability, and Employment
paper · pdf · doi:10.48550/arxiv.2312.10388
openalex publication_date 2023/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the causal impact of credit lines on spending, previous studies have employed causal estimators, based on direct regression (DR), inverse propensity weighting (IPW), and double machine learning (DML) to estimate the treatment effect. However, these estimators do not consider the notion that an individual's spending can be understood and represented as a distribution, which captures the range and pattern of amounts spent across different orders. By disregarding the outcome as a distribution, valuable insights embedded within the outcome distribution might be overlooked. This paper develops a distribution-valued estimator framework that extends existing real-valued DR-, IPW-, and DML-based estimators to distribution-valued estimators within Rubin's causal framework. We establish their consistency and apply them to a real dataset from a large e-commerce platform. Our findings reveal that credit lines positively influence spending across all quantiles; however, as credit lines increase, consumers allocate more to luxuries (higher quantiles) than necessities (lower quantiles).