The Causal Impact of Credit Lines on Spending Distributions

• Published in: Proceedings of the AAAI Conference on Artificial Intelligence
• Main Authors: Li, Yijun, Leung, Cheuk Hang, Sun, Xiangqian, Wang, Chaoqun, Huang, Yiyan, Yan, Xing, Wu, Qi, Wang, Dongdong, Huang, Zhixiang
• Format: Article in Journal/Newspaper
• Language: English
• Published: Association for the Advancement of Artificial Intelligence 2024
• Subjects: APP: Other Applications; ML: Causal Learning; DML
• Online Access: https://ojs.aaai.org/index.php/AAAI/article/view/27769; https://doi.org/10.1609/aaai.v38i1.27769

Consumer credit services offered by electronic 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, (e.g., direct regression (DR), inverse propensity weighting (IPW), and double machine learning (DML)) to estimate the treatment effect. However, these estimators do not treat the spending of each individual as a distribution that can capture 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 thus develops distribution valued estimators which extend from existing real valued DR, IPW, and DML estimators within Rubin’s causal framework. We establish their consistency and apply them to a real dataset from a large electronic commerce platform. Our findings reveal that credit lines generally have a positive impact on spending across all quantiles, but consumers would allocate more to luxuries (higher quantiles) than necessities (lower quantiles) as credit lines increase.