You are a Product Analytics/Data Science partner for an ads ranking/recommendation team. Facebook has shipped (or plans to ship) a **new ad recommendation algorithm** and believes it is better. ## Part A: How would you evaluate whether it is better? Design an evaluation plan assuming you *can* run an experiment. In your answer: - Define the **goal** and key stakeholders (advertisers, users, FB revenue). - Propose a set of **metrics** with tradeoffs: - **Primary metric** (choose one and justify) - **Diagnostic metrics** to explain movement - **Guardrail metrics** to prevent harm (user experience, platform integrity, advertiser outcomes) - Choose the **unit of randomization** (e.g., user, session, advertiser) and explain interference risks (marketplace effects, repeated exposure, learning effects). - Discuss basics of experiment execution: ramp plan, duration, power/MDE considerations, data quality checks (SRM), and how you would interpret heterogeneous impacts. ## Part B: What if 50/50 randomization is not feasible? Sometimes a 50/50 split is not appropriate (e.g., risk, capacity limits, model learning/feedback loops, or advertiser delivery constraints). Propose **at least two** valid alternatives for randomization or rollout (e.g., unequal allocation, phased ramp, cluster randomization, switchback, geo split), and explain: - When each approach is appropriate - Key pitfalls (bias, interference, novelty effects) - How analysis changes (e.g., variance, sequential monitoring, CUPED) ## Part C: If you cannot run a controlled test If you are **not allowed** to run an online A/B test, how would you decide what to recommend to users and/or whether the new algorithm is better? - Propose an approach using observational/offline data. - Address confounding and selection bias. - For recommendations (especially cold-start users), describe a reasonable strategy that balances relevance and exploration. Be explicit about assumptions and failure modes.