Reduce airport ride cancellations under causal constraints

Question

You are a data scientist supporting an airport rides / airport pickups team at a ride-hailing marketplace. Airport pickups differ operationally from city pickups, and cancellation rates are high, which reduces marketplace efficiency and worsens the experience for both riders and drivers.

Key context that makes this hard:

• Drivers typically join an airport queue governed by FIFO or priority rules. A cancellation by either driver or rider can be especially expensive: the driver may forfeit their queue position and have to exit the holding lot and rejoin.
• Airport riders are a distinct segment: pickup-zone navigation is confusing, many are one-time users, and trip intent (business vs personal, luggage, group) varies.
• Network effects / interference are likely: changing dispatch, pricing, or guidance for some users affects others waiting in the same shared queue, so SUTVA is violated.
• Standard experimentation is difficult: geo tests are hard because there are few airports and spillover occurs; switchbacks may be operationally risky; and diff-in-diff is biased by strong time-varying confounding such as flight arrival waves, weather, events, and seasonality.

Goal: lower the airport trip cancellation rate without harming marketplace health.

Assume access to standard marketplace logs (trip lifecycle events, dispatch events, queue position changes, app events), driver/rider attributes, and airport / terminal / pickup-zone identifiers.

Answer the following:

1. Problem framing & metrics. Define supply, demand, and marketplace health for airport pickups. Propose a primary metric (or small set) for "reduce cancellations" plus diagnostic and guardrail metrics. Be explicit about definitions: what counts as a cancellation, by whom, and over what time windows. Discuss segmentation (business vs personal, first-time vs repeat, terminal / pickup-zone complexity) and how it guards against Simpson's paradox.

2. Hypotheses. Generate plausible, testable hypotheses for why cancellations happen at airports — for (a) drivers and (b) riders — and include at least one behavioral / psychology hypothesis. For each, name the signals you would look for.

3. Causal identification / experiment design. Given interference from the shared queue, time-varying confounding, and a limited number of airports (you cannot turn the feature off for an entire region), propose one or more practical designs to estimate the causal impact of an intervention. Address how each handles interference, time variation, and operational constraints. Cover the spectrum from randomized to quasi-experimental approaches.

4. Satisfaction measurement, data needs, biases, and communication. Propose data-driven proxy metrics for driver and rider satisfaction specific to airports, and explain their limitations. List the data you would need and the main confounders / biases you would worry about. Explain how you would communicate the tradeoffs of an intervention to stakeholders.

Overview: A data scientist onsite product-sense and causal-inference case: design a measurement framework, hypotheses, and experimentation strategy to reduce airport ride cancellations in a ride-hailing marketplace without harming marketplace health. It tests metric trees, interference-aware experiment design (switchback, partial-population, encouragement/IV), satisfaction proxies, confounder handling, and stakeholder communication.