How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Upstart.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Upstart during technical interviews.

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

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:

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

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

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

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

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?

Quick Overview

Context

Task

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

Please address:

Experimental design: randomization unit(s), holdouts, timing windows, and any clustering.

Primary and secondary success metrics to track.

How you will detect and account for spillovers on untreated drivers.

How you will identify causal impact in the presence of interference (e.g., geographic clustering, holdout zones, difference-in-differences, network interference adjustments).

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

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

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

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?

Design Experiment to Measure Airport Surge-Pricing Impact

Quick Overview

Design Experiment to Measure Airport Surge-Pricing Impact

Design Experiment to Measure Airport Surge-Pricing Impact

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

Context

Task

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

Design Experiment to Measure Airport Surge-Pricing Impact

Quick Overview

Design Experiment to Measure Airport Surge-Pricing Impact

Design Experiment to Measure Airport Surge-Pricing Impact

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

Context

Task

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer