🤖 AI Summary
This study addresses the challenge of accurately identifying the causal effect of new supply on total transaction volume or value in heterogeneous-product two-sided markets. The authors propose a novel framework that integrates double (debiased) machine learning with hierarchical Bayesian modeling, innovatively incorporating a product-segment similarity metric from spatial economics literature as a key feature to characterize the incremental impact of supply across distinct market segments. By effectively combining causal inference techniques with domain-specific prior knowledge, the approach enhances both estimation accuracy and interpretability. Empirical results on Airbnb data demonstrate that the model not only yields substantially more precise and interpretable estimates but also exhibits robust out-of-sample predictive performance.
📝 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.