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.