Summary of A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks, by Zhe Yu et al.
A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks
by Zhe Yu, Chi Xia, Shaosheng Cao, Lin Zhou
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to estimate the consumer elasticity with ride-hailing subsidies in the presence of confounding effects. By introducing a consumer subsidizing system, the authors effectively capture the relationship between subsidy propensity and the treatment effect while maintaining a lightweight online environment. The study employs causal inference techniques to estimate the uplift effect at different subsidy levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ride-sharing services use incentives like discounts to encourage people to order more rides. This helps grow the market. Researchers used special statistical tools to figure out how much customers are affected by these discounts. However, there can be other factors that affect customer behavior, making it hard to get an accurate result. The authors created a new way to understand how people respond to different discount levels while keeping things simple online. |
Keywords
» Artificial intelligence » Inference
