Design Experiment to Measure Airport Surge-Pricing Impact

Quick Overview

Design Experiment to Measure Airport Surge-Pricing Impact evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design Experiment to Measure Airport Surge-Pricing Impact

Company: Upstart

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

##### Scenario Measuring the causal impact of airport surge-pricing push notifications on driver supply ##### Question How would you design an experiment to measure whether the push notification increases driver supply at the airport? Which primary and secondary success metrics would you track, and how would you account for spill-over effects on untreated drivers? Describe how you would establish causal impact while handling interference between treated and control drivers. ##### Hints Consider geographic clustering, holdout zones, difference-in-differences, network interference adjustments.

Quick Answer: Design Experiment to Measure Airport Surge-Pricing Impact evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Design Experiment to Measure Airport Surge-Pricing Impact

Experiment Design: Causal Impact of Airport Surge-Pricing Push Notifications on Driver Supply

Context

You operate a two-sided ride-hailing marketplace. A new push notification is sent to eligible drivers when the airport is in surge, aiming to attract more drivers to the airport. Drivers within and around the airport can see and respond to the push at overlapping times, so interference (spillovers) between treated and untreated drivers is plausible.

Task

Design an experiment to measure whether the push notification causally increases driver supply at the airport while handling potential interference.

Please address:

  1. Experimental design: randomization unit(s), holdouts, timing windows, and any clustering.
  2. Primary and secondary success metrics to track.
  3. How you will detect and account for spillovers on untreated drivers.
  4. How you will identify causal impact in the presence of interference (e.g., geographic clustering, holdout zones, difference-in-differences, network interference adjustments).

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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