In this episode, we talk about why Lyft needed to understand which rider experiences drive long-term retention. We discuss how raw observational data can produce misleading conclusions because of confounders such as geography, and how Lyft's Rider Experience Score addresses this challenge using Augmented Inverse Propensity Weighting. This doubly robust causal inference method adjusts for those confounders and produces experiment-quality estimates without running an experiment.

For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/my-starter-project-on-the-lyft-rider-data-science-team-86a60dddd935