This one took me a minute to get my footing.
Start by defining the network effect and identifying the unit of interference (e.g., users connected via messaging). Then propose a randomization unit that balances bias reduction and practical constraints, such as clusters or ego networks, and justify it with trade-offs. Finally, outline statistical methods like cluster-based variance estimation, exposure modeling, or switchback designs to detect and mitigate interference.
Pro tip: Emphasize that the choice of randomization unit should be driven by the specific interference mechanism and the metric's sensitivity to spillovers, not just convenience. Mentioning that you'd run a small pilot to measure the degree of interference before scaling up shows practical maturity.
Clarify how the feature creates network effects (e.g., messages sent to friends) and identify key metrics that could be affected by spillovers. This guides the choice of randomization unit.
Consider units like user, ego network, or cluster (e.g., graph community). Justify based on bias-variance trade-off, sample size, and feasibility. For Messenger, ego networks or clusters often balance interference and power.
Use techniques like cluster randomization, switchback designs, or exposure-based randomization (only randomize users with limited cross-group ties). Ensure proper power analysis accounting for intra-cluster correlation.
Use methods like cluster-robust standard errors, causal inference with interference (e.g., Aronow-Samii bounds), or model-based approaches (e.g., network exposure models). Consider variance reduction via CUPED or regression adjustment.
Run A/A tests or diagnostic checks to measure residual interference. If interference is high, consider alternative designs like switchback or time-based randomization. Communicate limitations and sensitivity analyses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.