Analyze Algorithm's Impact on Diverse Demographics and Validate Causes
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Analyze Algorithm's Impact on Diverse Demographics and Validate Causes 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.
Scenario
A/B test for a new ad-ranking algorithm shows a 5% overall CTR lift but 100% lift for Indian males aged 18-24.
Question
The experiment yields a 5% overall CTR increase but 100% for Indian males 18-24. What analyses would you run to decide whether to launch the algorithm? List possible root-causes for this heterogeneous effect and how you would validate them. What additional data or follow-up experiments are needed before rollout?
Hints
Think heterogeneous treatment effects, sampling bias, guardrails, segmentation, and long-term business impact.
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