Switchback Experiment Design: Reducing Cold-Food Incidents for Bike Couriers

You are optimizing a delivery marketplace feature suspected to reduce cold-food incidents for bike couriers in dense zones. Design a 2-week switchback experiment at the city level that toggles the feature ON/OFF by equal-length time slots within each city.

Address the following precisely:

A) Randomization

• Choose a slot length L given an average order lifecycle of 45 minutes and a driver relocation/carryover horizon of 30 minutes. Justify L to minimize contamination across slot boundaries.
• Describe a block-randomization scheme that balances day-of-week and peak hours while preventing predictability.

B) Assignment vs Exposure

• Define the difference between slot-level assignment (Intention-to-Treat) and realized exposure when some units operate in OFF slots but pick up spillover demand from neighboring ON slots.
• Specify what goes in the numerator and denominator for the primary metric (cold-food rate among biker deliveries), and show two denominator variants: (1) include all deliveries (condition_label = 0 and 1) vs (2) include only deliveries with condition_label = 1.

C) Analysis Model

• Write the exact regression you would run (formula notation is fine) with city fixed effects and slot-of-week fixed effects, and cluster-robust SEs at the city×slot level.
• Explain how you would incorporate pre-period baselines or covariates (e.g., weather, surge, courier mix) for precision.

D) Power

• Given: baseline cold-food rate = 6%, target relative reduction = 10% (MDE = 0.6pp), average 120 eligible orders per slot, intracluster correlation (ICC) at the slot level = 0.02, and 14 days.
• Estimate the number of switchbacks (ON↔OFF transitions per city) needed for 80% power at α = 0.05. State assumptions and show the core calculation or code you would use.

E) Diagnostics

• List concrete randomization checks and balance tests you will run, and how you would test for carryover (e.g., leading indicators, excluding boundary intervals).

F) Robustness

• How would you handle partial compliance, missing telemetry, or shocks (major events) mid-test?
• Describe a principled decision rule to stop, extend, or rerun the test.