A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks
Machine Learning
2024-08-06 v1 Machine Learning
Abstract
In the ride-hailing industry, subsidies are predominantly employed to incentivize consumers to place more orders, thereby fostering market growth. Causal inference techniques are employed to estimate the consumer elasticity with different subsidy levels. However, the presence of confounding effects poses challenges in achieving an unbiased estimate of the uplift effect. We introduce a consumer subsidizing system to capture relationships between subsidy propensity and the treatment effect, which proves effective while maintaining a lightweight online environment.
Cite
@article{arxiv.2408.02065,
title = {A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks},
author = {Zhe Yu and Chi Xia and Shaosheng Cao and Lin Zhou},
journal= {arXiv preprint arXiv:2408.02065},
year = {2024}
}