This part tripped me up more than I expected.
Start by clarifying the goal: the change should improve user experience and engagement, not just notification handling. Then propose using historical logs to simulate the new sorting and compare key metrics like click-through rates and session time, while controlling for confounders and checking for novelty effects. Finally, discuss limitations and how to validate with a small experiment.
Pro tip: Emphasize that observational data can only show correlation, so you need to carefully define the counterfactual and consider using techniques like propensity score matching or instrumental variables to approximate causality. Also, mention that even if offline metrics look promising, you should run a small online experiment to detect unexpected effects.
Define what 'beneficial' means: likely increased user engagement (e.g., more account switches, higher session time) and satisfaction, without harming notification interaction. Identify primary and guardrail metrics.
Use historical logs to reconstruct user sessions and simulate the new sorting order. For each session, determine which account would be on top under the new rule and compare to the actual top account.
Compare metrics like click-through rate on the top account, time to switch accounts, and overall engagement between the simulated new order and the actual order. Use statistical tests to assess significance.
Control for factors like user activity level, number of accounts, and notification frequency. Consider methods like stratification, regression, or matching to isolate the effect of sorting.
Acknowledge that observational data cannot prove causality. Recommend a small A/B test to validate findings, and discuss potential novelty effects or long-term impacts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around the core experiment design components: randomization unit, eligibility, metrics, sample size, and pitfalls. Emphasize causal inference and practical considerations specific to Meta's scale and product context.
Pro tip: Acknowledge that changing sort order can have network effects or cannibalization, and propose guardrail metrics to detect them. Also, mention that you'd run a pre-experiment power analysis and consider sequential testing if peeking.
State the hypothesis that sorting by most-notifications increases engagement. Choose the randomization unit (e.g., user-level) and justify why it's appropriate for this change.
Specify which users are eligible (e.g., active users with multiple accounts) and how they are assigned to control (most-recent-visit) or treatment (most-notifications).
Define primary metrics (e.g., notifications clicked, session duration) and guardrail metrics (e.g., user retention, complaints) to monitor unintended consequences.
Estimate required sample size using power analysis, considering baseline metrics, minimum detectable effect, and traffic. Determine experiment duration to achieve sufficient power.
Discuss potential pitfalls such as novelty effects, network effects, and metric dilution, and propose ways to mitigate them (e.g., holdout groups, long-term monitoring).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.