09-15, 10:00–10:30 (Europe/Amsterdam), Foo (main)
In this talk, we outline how we introduced causality into our machine learning models within the core checkout and onboarding experiences globally, thereby strongly improving our key business metrics. We discuss case studies, where experimental data were combined with machine learning in order to create value for our users and personalize their experiences, and we share our lessons learned with the goal to inspire attendees to start incorporating causality into their machine learning solutions. Additionally, we explain how the open source Python package developed at Uber, CausalML, can help others in successfully making the transition from correlation-driven machine learning to causal-driven machine learning.
In this talk, we outline how we introduced causality into our machine learning models within the core checkout and onboarding experiences globally, thereby strongly improving our key business metrics. We discuss case studies, where experimental data were combined with machine learning in order to create value for our users and personalize their experiences, and we share our lessons learned with the goal to inspire attendees to start incorporating causality into their machine learning solutions. Additionally, we explain how the open source Python package developed at Uber, CausalML, can help others in successfully making the transition from correlation-driven machine learning to causal-driven machine learning.
No previous knowledge expected
Leading the Payments Machine Learning team at Uber working on Anomaly Detection, Personalization & Fraud Detection within the Onboarding and Checkout experiences at Uber using Contextual Bandits, Uplift Modelling & Reinforcement Learning.