Explain Algorithm's Disproportionate Impact on Demographic Segments

Q: Explain Algorithm's Disproportionate Impact on Demographic Segments

This question evaluates a data scientist's ability to analyze experimental results, reason about causal inference and subgroup heterogeneity, and interpret CTR lifts in ad-ranking contexts, and is categorized under Analytics & Experimentation.

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

Question

Ad-Ranking A/B Test: Interpreting Heterogeneous CTR Lifts

Context

You ran a standard A/B experiment for a new ad-ranking algorithm. The primary metric is CTR (clicks ÷ impressions). The experiment shows:

Overall lift: +5% relative CTR
Specific segment (Indian males, age 18–24): +100% relative CTR

Assume randomization at the user level, with typical ad auction dynamics and repeated exposures per user.

Questions

Hypotheses: What could explain an overall +5% lift while a specific demographic shows +100%?
Statistical validity: How would you validate that the segment lift is statistically significant and not due to random noise or confounding?
Pre-rollout diligence: What additional metrics, slices, and checks would you examine before a global rollout?

Explain Algorithm's Disproportionate Impact on Demographic Segments

Ad-Ranking A/B Test: Interpreting Heterogeneous CTR Lifts

Context

Questions

Solution

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