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

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

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

Last updated: Mar 29, 2026

Quick Overview

This question evaluates competency in A/B test interpretation, detection and validation of heterogeneous treatment effects, causal inference, segmentation analysis, and experiment diagnostics for CTR metrics.

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.

Solution

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