Following the initial analysis, we developed a revised policy based on the estimated effects. To validate our proposed policy and the ATE, however, we still needed to run an A/B experiment. As part of a series of experiments intended to improve the payment experience for our customers, we ran an experiment where we significantly reduced the amount of upward rounding in the initial authorization buffer. The results validated the insights from our regression discontinuity analysis and generated a significant increase in order volume on Instacart through a reduction in the number of card declines!

By reducing frictions associated with the initial authorization buffer amounts, we were able to generate a win-win for both customers and Instacart, on average. However, within the average treatment effects may lie important heterogeneity. More broadly, our series of experiments pointed to the realization that adjusting the initial authorization buffer is just the start.

For some users, the initial authorization is a burdensome friction that needs to be reduced. However, reducing the initial authorization amount has the undesired consequence of increasing the likelihood that a second authorization is needed. For many customers, the subsequent authorization attempts may be a confusing and ultimately poor order experience. Our experiments in this domain are building towards the vision of an authorization and payment platform that can tailor the initial and subsequent authorization rulesets based on current order context and past customer interactions. Our rounding experiment was a stepping stone in this direction.