This was basically six questions wrapped in one.
Structure your answer around cluster-based randomization as the core design choice, explaining why standard user-level A/B testing fails when network spillovers exist. Walk through each component systematically — design, estimands, power, analysis, guardrails, and graph dynamics — demonstrating that you understand both the statistical theory and the practical engineering constraints at Meta's scale.
Pro tip: Explicitly name the two estimands — the Direct Treatment Effect (DTE) and the Indirect/Spillover Effect (ISE) — and explain how your design isolates each one; interviewers at Meta love candidates who speak precisely about what they're actually measuring rather than vaguely gesturing at 'network effects.'
Explain that user-level randomization violates SUTVA (Stable Unit Treatment Value Assumption) because a treated user's behavior (e.g., sending invites, using hashtags) directly affects control users in their network, contaminating the control group and biasing both treatment and control estimates.
Partition the social graph into dense, minimally-connected clusters (e.g., using community detection algorithms like Louvain or ego-network partitioning) and randomize at the cluster level, assigning entire clusters to treatment or control to contain spillovers within clusters and prevent cross-arm contamination.
Define the Direct Treatment Effect (DTE) by comparing treated users in treatment clusters vs. control users in control clusters, and the Indirect Spillover Effect (ISE) by comparing untreated neighbors of treated users vs. control users in fully-control clusters; use a two-stage regression or Horvitz-Thompson estimator with cluster-robust standard errors to account for within-cluster correlation.
Run power calculations at the cluster level (not user level), accounting for intra-cluster correlation (ICC) via a design effect (DEFF = 1 + (m-1)*ICC), which typically requires significantly more users than a standard A/B test; set guardrails on core health metrics (DAU, content integrity signals, user reports) at both the cluster and individual level to catch unintended viral harms early.
Address graph evolution by snapshotting cluster assignments at experiment launch and freezing them (new edges don't reassign users mid-flight), then post-hoc analyze edge additions as a sensitivity check; flag clusters with high cross-arm edge growth as potentially contaminated and consider excluding or down-weighting them in the final analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.