Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

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Q: What difficulty level is this interview question?

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

Q: What role is this question designed for?

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

Question

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

A/B Test: Heterogeneous Lift in CTR for a New Ad-Ranking Algorithm

Context

You ran a user-level A/B test of a new ad-ranking algorithm. The reported result is a 5% overall relative lift in click-through rate (CTR), but a 100% relative lift for the subgroup "Indian males aged 18–24." Assume CTR is clicks/impressions, randomization is at the user level, and the test lasted long enough to get initial readouts.

Task

What analyses would you run to decide whether to launch the algorithm?
List plausible root causes for this heterogeneous treatment effect (HTE) and how you would validate each cause.
What additional data or follow-up experiments are needed before rollout?

Hint: Consider heterogeneous treatment effects, sampling bias, guardrails, segmentation, and long-term business impact.

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 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?

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

Quick Overview

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

A/B Test: Heterogeneous Lift in CTR for a New Ad-Ranking Algorithm

Context

Task

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer