Jizhou Liu, Azeem M. Shaikh, Panos Toulis
arXiv 8 Apr 2025 · Statistics — Methodology
arXiv:2504.06215 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Masoero, L., Vijaykumar, S., Richardson, T. S., McQueen, J., Rosen,… (2026) Multiple randomization designs: estimation and inference with interference | 0.976 | 14 | 5 | 93% |
| 2 | Comola, M. and Prina, S (2021) Treatment effect accounting for network changes | 0.874 | 6 | 3 | 67% |
| 3 | Puelz, D., Basse, G., Feller, A. and Toulis, P (2021) A Graph-Theoretic Approach to Randomization Tests of Causal Effects under General Interference self | 0.874 | 6 | 3 | 67% |
| 4 | Zhao, A. and Ding, P (2021) Covariate-adjusted fisher randomization tests for the average treatment effect | 0.843 | 4 | 3 | 75% |
| 5 | Basse, G. W., Feller, A. and Toulis, P (2019) Randomization tests of causal effects under interference self | 0.811 | 4 | 2 | 100% |
| 6 | Wu, J. and Ding, P (2021) Randomization tests for weak null hypotheses in randomized experiments | 0.737 | 3 | 3 | 67% |
| 7 | Bajari, P., Burdick, B., Imbens, G. W., Masoero, L., McQueen, J., Ri… (2023) Experimental Design in Marketplaces | 0.737 | 3 | 2 | 100% |
| 8 | Johari, R., Li, H., Liskovich, I. and Weintraub, G. Y (2022) Experimental design in two-sided platforms: An analysis of bias | 0.737 | 3 | 2 | 100% |
| 9 | Athey, S., Eckles, D. and Imbens, G. W (2018) Exact p-values for network interference | 0.644 | 2 | 2 | 100% |
| 10 | Chung, E. and Romano, J. P (2013) Exact and asymptotically robust permutation tests | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 29 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Randomization Tests in Switchback Experiments | 0.405 | 1 | 1 |
| 2 | Randomization Tests in Randomized Saturation Designs | 0.405 | 1 | 1 |