A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks
This addresses the challenge of optimizing subsidies for market growth in the ride-hailing industry, representing an incremental improvement in causal inference applications.
The paper tackles the problem of biased uplift effect estimation in ride-hailing subsidies due to confounding effects, introducing a consumer subsidizing system that effectively captures relationships between subsidy propensity and treatment effect while maintaining a lightweight online environment.
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.