Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach
arXiv:2606.30999v1 Announce Type: new 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 do
The proliferation of two-sided marketplaces and advancements in causal machine learning techniques enable more precise economic analysis of supply dynamics.
Understanding supply incrementality helps marketplace operators optimize resource allocation and growth strategies while providing insights into market efficiency and competition.
Marketplace management can move from broad assumptions to data-driven, causal understandings of how supply changes impact demand and transaction value across segments.
- · Two-sided marketplaces
- · Data scientists and economists
- · Platforms with heterogeneous products
- · Marketplace operators relying on intuition
- · Inefficient resource allocators
Marketplaces can more accurately predict the impact of incentives and supply-side policies.
Optimized supply could lead to increased efficiency and better pricing for consumers within these platforms.
This optimized allocation could concentrate market power further in dominant platforms capable of such advanced analytics.
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