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

by   Teng Ye, et al.

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 procedure and findings shed light on how to analyze and optimize large-scale online field experiments in general.


Interpretable Deep Causal Learning for Moderation Effects

In this extended abstract paper, we address the problem of interpretabil...

The Comparison of Methods for Individual Treatment Effect Detection

Today, treatment effect estimation at the individual level is a vital pr...

Methods for Individual Treatment Assignment: An Application and Comparison for Playlist Generation

We present a systematic analysis of causal treatment assignment decision...

A/B Testing in Dense Large-Scale Networks: Design and Inference

Design of experiments and estimation of treatment effects in large-scale...

Interpretable Multiple Treatment Revenue Uplift Modeling

Big data and business analytics are critical drivers of business and soc...

Please sign up or login with your details

Forgot password? Click here to reset