Design an experiment for delay drivers

Q: Design an experiment for delay drivers

This question evaluates experimental design and causal inference skills — including randomized and quasi-experimental identification, power and sample-size reasoning, handling interference/spillovers, and pre-registered estimands — within an operational analytics context.

Q: 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.

Q: What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Capital One.

Q: What role is this question designed for?

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

Question

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Estimating the Causal Effect of Reducing Gate Turnaround on Flight Delays

Context: Operational drivers of flight delays (e.g., gate turnaround, taxi-out time, airport busyness) are highly collinear, making causal interpretation from observational models unreliable. You are asked to design a credible empirical strategy to estimate the causal effect of reducing gate turnaround time on the probability a flight departs >15 minutes late.

Define the outcome as a binary indicator: Delay15 = 1 if scheduled departure to wheels-off delay > 15 minutes; 0 otherwise. "Gate turnaround" is the time from on-block (arrival at gate) to off-block (pushback) for a given flight.

Tasks:

Randomized design (cluster-randomized by airport × day block)
- Specify: unit of randomization, blocking/stratification, treatment definition, primary outcome, guardrails, and how you’ll mitigate interference/spillovers.
Powering
- Given: baseline delay rate p0 = 24%, minimum detectable effect (MDE) = 2 percentage points, alpha = 0.05, power = 0.80, average 200 flights per cluster, ICC = 0.15.
- Compute the design effect and required clusters per arm. Show formulas; an approximate numeric answer is acceptable.
If randomization is infeasible
- Outline a credible quasi-experiment: Difference-in-Differences (DiD) with staggered rollout or an Instrumental Variables (IV) approach.
- State identifying assumptions, pre-trend checks, and robustness tests.
Analysis plan
- Pre-register estimands (ATE, and CATE by airport tier), adjustments for multiplicity, and a missing-data plan.
- Include a plan for heterogeneous effects and how to translate findings into an operational policy.

Design an experiment for delay drivers

Quick Overview

Estimating the Causal Effect of Reducing Gate Turnaround on Flight Delays

Solution

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