# The Causal Impact of Credit Lines on Spending Distributions

arXiv: https://arxiv.org/abs/2312.10388 · HTML: https://mlsanity.com/paper/2312.10388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 16 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [Causal Inference on Distribution Functions](https://mlsanity.com/paper/2101.01599/) (2021)
- [Hybrid Wasserstein Distance and Fast Distribution Clustering](https://mlsanity.com/paper/1812.11026/) (2018)
- [Causal inference with a functional outcome](https://mlsanity.com/paper/2304.07113/) (2023)
- [The Causal Learning of Retail Delinquency](https://mlsanity.com/paper/2012.09448/) (2020)

## Built on by (most cited first, 1 of 1)

- [Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators](https://mlsanity.com/paper/2402.18392/) (2024)

## Abstract (arXiv)

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).