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The Incentives Platform at Lyft

The Incentives Platform team at Lyft has developed a platform for applying new methodologies at the intersection of causal inference, machine learning, and reinforcement learning to problems at scale. We utilize heterogeneous treatment effect algorithms to predict how different users (riders, drivers) will respond to a specific treatment (coupon, incentive, message, etc.). We then can apply various optimization algorithms to choose which users get which treatment while using bandit methodologies to balance an explore/exploit trade-off. This platform dramatically increases the degree to which we can customize the user experience and hit business goals while reducing the operational load of doing so. This platform lets us understand how our users differ; letting us optimally target users based on individual treatment effect predictions; and evaluate the results of these predictions. The platform is built in a flexible way to allow us to plug-and-play with different algorithms, which lets us compare performance and develop improvements. We have integrated Off-Policy Evaluation into the platform allowing us to make unbiased (backtesting) evaluations of causal effects, without needing to run an AB test. While the scale of our data and complexity of these algorithms requires substantial engineering infrastructure, we have built the platform in a modular way that allows for separation between the science and engineering code. This makes it easy for data scientists to iterate on these models without worrying (as much) about infrastructure or distributed systems.