This question evaluates competence in experiment design and causal inference for marketplace interventions, covering choice of randomization unit and mitigation of interference/spillovers, stratification and covariate adjustment, power and sample‑size calculations, detection of novelty/steady‑state effects, heterogeneity estimation, and pre-registered guardrail decision rules. It is commonly asked in the Analytics & Experimentation domain because interviewers need to assess both conceptual understanding of causal inference and the practical application of randomized designs and operational measurement to ensure robust, contamination‑aware evaluation of policy changes.

DoorDash plans to test a dispatch policy that allows a dasher to pick up two nearby orders in one trip during peak hours. The goal is to estimate the causal impact on:
Assume batching is only enabled for eligible orders (e.g., distance and timing constraints) and that peak hours are predefined per market. You must account for marketplace spillovers (dashers, restaurants, and customers interact within local zones).
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