This is the kind of question where saying 'just randomly split users' is the wrong answer and you know it the second you say it out loud.
Start by defining network effects and explaining why they violate the Stable Unit Treatment Value Assumption (SUTVA) in standard A/B tests. Then describe strategies to detect, measure, and mitigate interference, such as cluster-based randomization or switchback designs, and emphasize the trade-offs involved.
Pro tip: Mention that at Google, you'd leverage existing infrastructure like cluster randomization or interleaving, and always validate with a holdback or switchback experiment to quantify bias.
Clarify what type of interference exists (e.g., direct spillover, global effect) and how it violates SUTVA, making standard A/B tests biased.
Select a unit that minimizes interference, such as clusters (e.g., social groups, geographic regions) or time-based switchbacks, depending on the product.
Implement cluster randomization or switchback designs, ensuring sufficient power and accounting for intra-cluster correlation.
Use cluster-robust standard errors, mixed-effects models, or causal inference techniques to estimate treatment effects while accounting for interference.
Run holdback or A/A tests to detect residual bias, and consider complementary methods like interleaving or synthetic control to cross-validate results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.