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Machine Learning & Data Science / 37
hardMetaGoogleDatabricks

The customer's marketplace has network effects, so a user-level A/B test is biased. How do you measure the model's impact?

When treatment leaks between units, a clean A/B lies in both directions. The interference taxonomy, when to reach for switchback vs cluster vs geo designs, and the analysis trap that makes naive standard errors useless.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

When treatment leaks between units, a clean A/B lies in both directions. The interference taxonomy, when to reach for switchback vs cluster vs geo designs, and the analysis trap that makes naive standard errors useless.

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FEDITOR'S NOTE

The screen is whether the candidate recognizes that SUTVA is broken before proposing a design, and can name the failure direction: shared inventory or pricing makes a pricing model look worse than it is, while a recommender that trains on contaminated logs can look better. The reserved follow-up is the analysis side, that switchback observations are autocorrelated so naive standard errors understate uncertainty, and you need block-level or time-clustered inference; knowing the design is half, knowing the analysis is the staff half.

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