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Millions of drivers worldwide have enjoyed financial benefits and workschedule flexibility through a ride-sharing economy, but meanwhile they havesuffered from the lack of a sense of identity and career achievement. Equippedwith social identity and contest theories, financially incentivized teamcompetitions have been an effective instrument to increase drivers'productivity, job satisfaction, and retention, and to improve revenue over costfor ride-sharing platforms. While these competitions are overall effective, thedecisive factors behind the treatment effects and how they affect the outcomesof individual drivers have been largely mysterious. In this study, we analyzedata collected from more than 500 large-scale team competitions organized by aleading ride-sharing platform, building machine learning models to predictindividual treatment effects. Through a careful investigation of features andpredictors, we are able to reduce out-sample prediction error by more than 24%.Through interpreting the best-performing models, we discover many novel andactionable insights regarding how to optimize the design and the execution ofteam competitions on ride-sharing platforms. A simulated analysis demonstratesthat by simply changing a few contest design options, the average treatmenteffect of a real competition is expected to increase by as much as 26%. Ourprocedure and findings shed light on how to analyze and optimize large-scaleonline field experiments in general.

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