Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy

Teng Ye, Wei Ai, Lingyu Zhang, Ning Luo, Lulu Zhang, Jieping Ye, Qiaozhu Mei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Millions of drivers worldwide have enjoyed financial benefits and work schedule flexibility through a ride-sharing economy, but meanwhile they have suffered from the lack of a sense of identity and career achievement. Equipped with social identity and contest theories, financially incentivized team competitions have been an effective instrument to increase drivers' productivity, job satisfaction, and retention, and to improve revenue over cost for ride-sharing platforms. While these competitions are overall effective, the decisive factors behind the treatment effects and how they affect the outcomes of individual drivers have been largely mysterious. In this study, we analyze data collected from more than 500 large-scale team competitions organized by a leading ride-sharing platform, building machine learning models to predict individual treatment effects. Through a careful investigation of features and predictors, we are able to reduce out-sample prediction error by more than 24%. Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms. A simulated analysis demonstrates that by simply changing a few contest design options, the average treatment effect of a real competition is expected to increase by as much as 26%. Our procedure and findings shed light on how to analyze and optimize large-scale online field experiments in general.

Original languageEnglish (US)
Title of host publicationKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2368-2377
Number of pages10
ISBN (Electronic)9781450379984
DOIs
StatePublished - Aug 23 2020
Externally publishedYes
Event26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States
Duration: Aug 23 2020Aug 27 2020

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Country/TerritoryUnited States
CityVirtual, Online
Period8/23/208/27/20

Bibliographical note

Funding Information:
We thank Yan Chen for designing the team competition experiments and Hongtu Zhu for helpful discussions. This work is funded in part by the DiDi Research Partnership with the University of Michigan. Jieping Ye and Lingyu Zhang’s work is in part funded by the National Key Research and Development Program of China under grant 2018AAA0101100. Qiaozhu Mei’s work is in part supported by the National Science Foundation under grant no. 1633370.

Publisher Copyright:
© 2020 ACM.

Keywords

  • field experiment
  • individual treatment effect
  • machine learning
  • sharing economy
  • team competition

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