The Causal Impact of Credit Lines on Spending Distributions
This work addresses the need for more nuanced causal analysis in e-commerce by capturing spending distributions, offering incremental improvements for researchers and platforms.
The paper tackled the problem of estimating the causal impact of credit lines on consumer spending by developing a distribution-valued estimator framework that extends existing real-valued causal estimators, revealing that credit lines positively influence spending across all quantiles with a shift toward luxuries as credit lines increase.
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).