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Pricing Experiments in Matching Marketplaces under Interference: Designs and Estimators

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Arthur Delarue, Kleanthis Karakolios

Optimization and Control · Submitted 26 Feb 2025

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · asserted 0.93 read the record →

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arXiv ID2502.18839 (version 1)
Pith ID2PLCSY2K, Pith's permanent record of this paper and its review
CitationArthur Delarue, Kleanthis Karakolios. "Pricing Experiments in Matching Marketplaces under Interference: Designs and Estimators." arXiv:2502.18839 (2025). https://pith.science/paper/2PLCSY2K
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,885 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.36 · human participants 0.16 · controlled comparison 0.76 · randomized assignment 0.06 · states quantitative result 0.05 · compares with prior results 0.64 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Interference between treated and untreated units is a source of bias in marketplace experiments. background 1.00 0.08 0.04 asserted 1.00
s1 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. contribution 0.54 0.29 0.03 asserted 0.94
s2 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? gap 0.49 0.07 0.04 unclear 0.48
s3 We find that standard estimation techniques are biased, but the sign of this bias depends strongly on this design choice. result 1.00 0.93 0.26 asserted 1.00
s4 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. result 0.56 0.91 0.53 asserted 0.93
s5 We validate our findings both theoretically in a fluid limit setting, and numerically in a finite-sample setting. method 0.61 0.94 0.05 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.92 · evidence from proof or derivation 0.71.

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 2 sentences without a confident label

Compares with 1 paper
  • Though our work is methodologically most similar to [ref] , we reach similar conclusions to [ref] .

    2310.17165 · Related Work · 1.00

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