A Bias Correction Approach for Interference in Ranking Experiments

The authors are grateful to an anonymous firm for providing the data. The authors thank Dean Eckles, Simha Mummalaneni and Omid Rafieian for their detailed comments that have significantly improved the paper. Comments from the participants of the 2021 INFORMS Marketing Science Conference, the 2022 University of Texas at Dallas Bass Conference, and CODE MIT 2023 have helped the paper. The authors also thank seminar participants at Baruch College, Hong Kong Polytechnic University, Indian Institute of Management at Ahmedabad, Ivey Business School, Johns Hopkins University (Economics), Penn State University, Temple University, University of Miami, and Yale University for their feedback. The authors have no competing interests to declare.