This is the kind of question where you can talk for 20 minutes and still feel like you barely scratched the surface.
Start by defining a composite 'socialness' outcome using impression and interaction logs, with action weights derived from a utility model or empirical engagement value. Then design a quasi-experimental analysis that controls for ranking confounds via propensity score matching or inverse propensity weighting, and estimate the causal effect of friend content on social engagement using a regression with fixed effects or a difference-in-differences approach.
Pro tip: Emphasize that ranking confounds are the biggest threat to validity; propose using the ranking model's features as covariates or leveraging a randomized holdout in the ranking system to create a clean comparison.
Construct a composite engagement metric that captures social interactions (e.g., likes, comments, shares) weighted by their estimated value, using impression-level logs to attribute actions to content.
Derive weights from a utility model (e.g., willingness to pay, long-term value) or empirically from downstream outcomes like retention, ensuring they reflect true social value.
Use propensity score matching or inverse propensity weighting based on ranking features, or exploit a randomized ranking holdout, to isolate the effect of friend content from ranking bias.
Fit a regression model with user and content fixed effects, or a difference-in-differences design, to estimate the average treatment effect of friend content on the socialness outcome.
Conduct robustness checks, sensitivity analyses, and significance testing (e.g., bootstrap confidence intervals) to ensure the effect is not driven by unobserved confounders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the product goal and defining success metrics that capture both user engagement and ecosystem health. Then design an A/B experiment with a randomization unit that minimizes interference, and outline a ramp plan with power calculations, guardrails, and a rollout/rollback framework. Explicitly address novelty effects and cannibalization through measurement and mitigation strategies.
Pro tip: Propose using a cluster-randomized design (e.g., by user or social graph cluster) to account for network interference, and pre-register a holdback group to measure long-term novelty decay and cannibalization.
Identify primary KPIs (e.g., unconnected content engagement, overall feed engagement, user retention) and guardrail metrics (e.g., friends' content engagement, user satisfaction, report rate). Include both short-term and long-term indicators.
Choose a randomization unit (e.g., user, household, or social cluster) that reduces spillover effects. Consider cluster randomization or switchback designs if interference is high. Define treatment and control groups clearly.
Determine sample size and duration using power analysis, accounting for intra-cluster correlation if using cluster randomization. Plan a gradual ramp (e.g., 1%, 5%, 10%, 50%) to monitor early signals and limit risk.
Include a long-term holdout to measure novelty decay. Analyze cannibalization by tracking friends' content engagement and overall time spent. Use difference-in-differences or cohort analysis to separate novelty from sustained effects.
Define go/no-go criteria based on primary KPIs and guardrails. Set up automated monitoring and alerts for guardrail violations. Outline a rollback plan if negative impacts are detected, and a phased rollout if successful.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.