用因果机器学习估算平台新增供给对交易量的影响。
Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach
- 结合双重机器学习与分层贝叶斯框架，利用地理相似度特征建模。
- 在爱彼迎数据上验证，新增房源带来可量化交易增长。
- 适合关注平台供需关系的算法研究者与产品决策者。
在具有异质性产品的双边市场中，理解额外供给与市场结果（如总交易量或交易价值）之间的因果关系至关重要。本文以爱彼迎平台为例，研究了跨产品细分领域估计该关系的因果机器学习方法。方法结合双重/去偏机器学习与分层贝叶斯框架，利用地理空间文献中的产品细分相似性度量构建可解释特征。实验表明，该模型能提供合理的额外供给回报估计，并具备强泛化能力。
原文摘要 · Abstract (English)
In two-sided marketplaces with heterogeneous products, it is important to understand the causal relationship between additional supply and marketplace outcomes, such as the total quantity transacted or transaction value in the marketplace. This paper studies a causal machine learning approach to estimating this relationship across product segments. We use the Airbnb marketplace as an example, focusing on the impact of additional listing supply on total bookings, but the methodology applies to other two-sided marketplaces. Our approach combines double/debiased machine learning with a hierarchical Bayesian framework that leverages pre-existing knowledge as priors. We construct tractable and informative features for the model by leveraging measures of product segment similarity from the geospatial literature. We find that such a model provides plausible estimates of the marketplace returns to additional supply and strong out of sample performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。