Start by framing the problem as a randomized experiment where the difference in means provides an unbiased estimate of the average treatment effect (ATE). Then, discuss how to compute a 95% confidence interval using either a t-test or bootstrap, and explain when and how to incorporate pre-treatment covariates to improve precision without introducing bias. Emphasize that covariates should be pre-treatment and used via regression adjustment or CUPED to reduce variance.
Pro tip: At Netflix, where metrics like minutes streamed are often heavy-tailed and noisy, using CUPED with pre-treatment covariates can dramatically increase power, but always pre-register the covariates to avoid p-hacking. Also, consider that user-level randomization may have interference or spillover effects, so check for consistency of behavior across devices.
Compute the difference in mean minutes streamed between the treatment and control groups. Since randomization ensures independence, this difference is an unbiased estimate of the ATE.
Use a two-sample t-test (or Welch's t-test if variances differ) to calculate the standard error and confidence interval. Alternatively, use bootstrap resampling if the metric distribution is highly skewed.
Evaluate whether pre-treatment covariates (e.g., historical viewing, device type) are predictive of the outcome. If they are, they can be used to reduce variance and increase precision.
Use regression adjustment (e.g., ANCOVA) or CUPED to incorporate covariates. Ensure covariates are pre-treatment and balanced across groups; if imbalance exists, adjust for it.
Check that covariate adjustment does not introduce bias (e.g., by verifying balance) and that the treatment effect estimate remains consistent. Report the adjusted ATE and CI, and discuss practical significance.
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