Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach
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Computer Science > Machine Learning
Title:Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach
Abstract: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.
| Comments: | 5 pages, 3 figures. Accepted at the KDD 2025 Workshop on Causal Inference and Machine Learning in Practice (not presented) |
| Subjects: | Machine Learning (cs.LG); Econometrics (econ.EM); Applications (stat.AP); Methodology (stat.ME) |
| Cite as: | arXiv:2606.30999 [cs.LG] |
| (or arXiv:2606.30999v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.30999
arXiv-issued DOI via DataCite (pending registration)
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