##### Question Uber Eats is considering a new feature: when a user places an order, they can optionally **add a donation** (to the merchant or a merchant-selected cause) during checkout. You are the data scientist who owns the evaluation. Produce a concise plan that an analytics/DS team could execute. 1. **Goal and hypotheses.** What is the main product goal of this feature? State a **primary** hypothesis and 2-4 **secondary** hypotheses, including at least one plausible **negative** effect. 2. **Metrics selection.** Define your **primary (success) metric(s)**, **diagnostic metrics** (to understand mechanisms), and **guardrail metrics** (to ensure no harm). Be explicit about definitions: unit of analysis, numerator/denominator, time window, and where in the funnel each metric is measured. 3. **Experiment design.** Describe an experiment plan, including: experiment/randomization unit (user/order/merchant), eligibility and experiment population, treatment arm(s) and control, duration and power/MDE considerations, and how you would handle repeated orders, interference/spillover, novelty effects, and heterogeneous treatment effects (e.g., by merchant type or user frequency). 4. **Risks and confounders.** List the major risks, biases, and marketplace effects (e.g., selection bias, cannibalization, instrumentation bugs, equilibrium effects) that could mislead conclusions, and how you would address each. 5. **Decision framework.** Spell out the launch / no-launch criteria, and how you would weigh tradeoffs (e.g., conversion down but retention up).