Start by framing the experiment as a randomized controlled trial at the appropriate unit (e.g., package or route), then systematically address each requirement: randomization, interference, metrics, analysis plan, power, ramp/rollback, anti-gaming, and fallback. Emphasize pre-registration and guardrail metrics to ensure rigor and safety.
Pro tip: At Amazon, always tie metrics to customer experience and long-term value; propose a switchback or cluster randomization if interference is likely, and pre-register the analysis plan to avoid p-hacking.
Choose the randomization unit (e.g., package, route, or time window) based on interference risk. Use stratified randomization by key covariates (e.g., package size, destination) to balance groups.
If interference is likely (e.g., shared resources), use cluster randomization (e.g., by fulfillment center) or switchback designs. Measure and adjust for spillover effects if present.
Define primary metric (e.g., allocation accuracy) and guardrail metrics (e.g., delivery time, cost). Conduct power analysis to determine sample size and duration, accounting for intra-cluster correlation if applicable.
Pre-register the analysis plan including statistical tests, subgroup analyses, and handling of missing data. Define ramp-up stages with rollback criteria based on guardrail metrics.
Implement anti-gaming measures (e.g., audit trails, anomaly detection). If clean randomization isn't feasible, propose quasi-experimental methods (e.g., difference-in-differences, propensity score matching) with sensitivity analyses.
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