Reduce airport cancellations under causal constraints

You are a Data Scientist on an airport rides team for a ride-hailing marketplace.

Airport rides differ from city rides:

• Drivers often enter an airport queue (FIFO/priority rules), so cancellations can impose large costs on drivers (they may lose their queue position, have to leave the holding lot, or re-enter).
• Riders at airports are a special segment (navigation to pickup zones, unfamiliarity, one-time users, business vs personal trips).
• There may be network effects / interference (one driver’s assignment affects others in the queue).
• Standard experimentation is hard:
  • Geo tests are difficult (only a few airports; spillover).
  • Switchback may not be feasible or could induce operational risk.
  • Diff-in-diff can be biased by time-varying confounding.
  • Synthetic control may be hard to operationalize.

Problem

Cancellations are high for airport pickups, hurting both marketplace efficiency and user experience.

Questions

1. Propose a measurement framework for driver experience and rider experience at airports.
   • Define primary metric(s) , diagnostic metrics , and guardrail metrics .
   • Discuss how you would handle segmentation (e.g., business vs personal, first-time vs repeat, pickup zone complexity).
2. Generate plausible hypotheses for why cancellations happen at airports.
   • Include both data-driven hypotheses and at least one behavioral/psychology hypothesis.
3. Propose an evaluation / causal strategy to test interventions to reduce cancellations, given:
   • interference/network effects,
   • queue dynamics,
   • time-varying demand/supply and seasonality,
   • limited number of airports.
4. Outline what data you would need, the main confounders/biases you’d worry about, and how you’d communicate tradeoffs to stakeholders.