Pricing Experiments in Matching Marketplaces under Interference: Designs and Estimators
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912247553458176 |
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| author | Delarue, Arthur Karakolios, Kleanthis |
| author_facet | Delarue, Arthur Karakolios, Kleanthis |
| contents | Interference between treated and untreated units is a source of bias in marketplace experiments. In this paper, we specifically consider pricing interventions, in which a platform seeks to adjust base pricing levels at the marketplace level in order to increase demand. In a matching marketplace, this type of experiment leads to a crucial design question: should the platform match treated and untreated units differently because they paid different prices? We find that standard estimation techniques are biased, but the sign of this bias depends strongly on this design choice. Bias can be reduced by using the ``shadow price estimator'', which relies on the optimal dual solution of the platform's supply-demand matching problem -- especially when the platform chooses to ignore pricing differences at matching time. We validate our findings both theoretically in a fluid limit setting, and numerically in a finite-sample setting. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_18839 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pricing Experiments in Matching Marketplaces under Interference: Designs and Estimators Delarue, Arthur Karakolios, Kleanthis Optimization and Control Interference between treated and untreated units is a source of bias in marketplace experiments. In this paper, we specifically consider pricing interventions, in which a platform seeks to adjust base pricing levels at the marketplace level in order to increase demand. In a matching marketplace, this type of experiment leads to a crucial design question: should the platform match treated and untreated units differently because they paid different prices? We find that standard estimation techniques are biased, but the sign of this bias depends strongly on this design choice. Bias can be reduced by using the ``shadow price estimator'', which relies on the optimal dual solution of the platform's supply-demand matching problem -- especially when the platform chooses to ignore pricing differences at matching time. We validate our findings both theoretically in a fluid limit setting, and numerically in a finite-sample setting. |
| title | Pricing Experiments in Matching Marketplaces under Interference: Designs and Estimators |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2502.18839 |