A Bias Correction Approach for Interference in Ranking Experiments

A bias-correction approach that can recover the total average treatment effect of a ranking algorithm based on past A/B tests even if those tests suffer from interference issues.

• Publication year

  2023

• Venue

  Marketing science (Providence, R.I.)

• Publication date

  2023-10-10

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.2139/ssrn.4021266
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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