Senior interview questionAsked at DoorDash · interview write-up
Target a free-delivery promotion at 20% of customers to maximize incremental orders per dollar: define the uplift estimand, the experiment to collect data and how you evaluate uplift models offline.
Model answer
The estimand is the conditional average treatment effect: the extra orders free delivery causes for a customer, divided by its cost.
Collect data with a randomised experiment, treating a random slice, so treatment is independent of features. Fit a T-learner, X-learner or uplift trees. You never see one customer's effect, so evaluate offline with Qini or uplift curves: rank holdout customers by predicted uplift and compare treated against control order rates in the top 20%, per dollar spent. Confirm with a live test of the targeted policy against random targeting.
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