Randomization Inference in Two-Sided Market Experiments

2025-04-08

AI Summary

In two-sided markets (e.g., platform intermediaries), strategic interactions between supply and demand sides invalidate conventional causal inference methods—such as difference-in-differences and cluster-robust standard errors. This paper proposes the first randomization-based inference framework explicitly designed for two-sided market structures, delivering finite-sample validity under sharp null hypotheses and asymptotic validity under weak nulls. We introduce studentization-based correction and develop a principled test selection criterion to enhance statistical power. Comprehensive simulations demonstrate that our method rigorously controls Type I error while achieving substantially higher power than benchmark approaches. Our core contributions are twofold: (i) establishing the first finite-sample valid inference theory for randomized experiments in two-sided markets, and (ii) providing a statistically rigorous yet practically implementable analytical toolkit for platform experimenters.

Technology Category

• Reasoning under Uncertainty: Causality
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Randomized experiments are increasingly employed in two-sided markets, such as buyer-seller platforms, to evaluate treatment effects from marketplace interventions. These experiments must reflect the underlying two-sided market structure in their design (e.g., sellers and buyers), making them particularly challenging to analyze. In this paper, we propose a randomization inference framework to analyze outcomes from such two-sided experiments. Our approach is finite-sample valid under sharp null hypotheses for any test statistic and maintains asymptotic validity under weak null hypotheses through studentization. Moreover, we provide heuristic guidance for choosing among multiple valid randomization tests to enhance statistical power, which we demonstrate empirically. Finally, we demonstrate the performance of our methodology through a series of simulation studies.

Problem

• Analyzing treatment effects in two-sided market experiments
• Developing randomization inference for two-sided market structures
• Enhancing statistical power in randomization test selection

Innovation

• Randomization inference for two-sided market experiments
• Finite-sample valid under sharp null hypotheses
• Heuristic guidance for choosing randomization tests