James Lee
James Lee is a data science manager on Uber's Marketplace team, working on rider incentives, Uber Pass and Uber Rewards. He is interested in causal machine learning and experimentation.
Research Papers
Improve User Retention with Causal Learning
S. Du, J. Lee, F. Ghaffarizadeh
User retention is a key focus for consumer based internet companies and promotions are an effective lever to improve retention. However, companies rely either on non-causal churn prediction to capture heterogeneity or on regular A/B testing to capture average treatment effect. In this paper, we propose a heterogeneous treatment effect optimization framework to capture both heterogeneity and causal effect. [...] [PDF]
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
User retention is a key focus for consumer based internet companies and promotions are an effective lever to improve retention. However, companies rely either on non-causal churn prediction to capture heterogeneity or on regular A/B testing to capture average treatment effect. In this paper, we propose a heterogeneous treatment effect optimization framework to capture both heterogeneity and causal effect. [...] [PDF]
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
