Working Paper

A Bias Correction Approach for Interference in Ranking Experiments

Ali Goli

University of Washington

Anja Lambrecht

London Business School

Hema Yoganarasimhan

University of Washington

Mar 28, 2022


Demonstrates the presence of interference bias in the Total Average Treatment Effect (TATE) estimates of ranking algorithms based on A/B tests and provides a novel solution that can recover the true TATE of a ranking algorithm based on past A/B tests, even if those tests suffer from a combination of interference issues.

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