Technology firms conduct randomized controlled experiments ("A/B tests") to learn which actions to take to improve business outcomes.In firms with mature experimentation platforms, experimentation programs can consist of many thousands of tests.To effectively scale experimentation, firms rely on decision rules: standard operating procedures for mapping the results of an experiment to a choice of treatment arm to launch to the general user population.Despite the critical role of decision rules in translating experimentation into business decisions, rigorous guidance on how to evaluate and choose decision rules is scarce.This paper proposes to evaluate decision rules based on their cumulative returns to business north star metrics.Although intuitive and easy to explain to decisionmakers, this quantity can be difficult to estimate, especially when experiments have weak signal-to-noise ratios.We develop a crossvalidation estimator that is much less biased than the naive plug-in estimator under conditions realistic to digital experimentation.We demonstrate the efficacy of our approach via a case study of 123 historical A/B tests at Netflix, where we used it to show that a new decision rule would have increased cumulative returns to the north star metric by an estimated 33%, directly leading to the adoption of the new rule.

When primary objectives are insensitive or delayed, experimenters may instead focus on proxy metrics derived from secondary outcomes. For example, technology companies often infer the long-term impacts of product interventions from their effects on short-term user engagement signals. We consider the meta-analysis of many historical experiments to learn the covariance of treatment effects on these outcomes, which can support the construction of such proxies. Even when experiments are plentiful, if treatment effects are weak, the covariance of estimated treatment effects across experiments can be highly biased. We overcome this with techniques inspired by weak instrumental variable analysis. We show that Limited Information Maximum Likelihood (LIML) learns a parameter equivalent to fitting total least squares to a transformation of the scatterplot of treatment effects, and that Jackknife Instrumental Variables Estimation (JIVE) learns another parameter computable from the average of Jackknifed covariance matrices across experiments. We also present a total covariance estimator for the latter estimand under homoskedasticity, which is equivalent to a k-class estimator. We show how these parameters can be used to construct unbiased proxy metrics under various structural models. Lastly, we discuss the real-world application of our methods at Netflix.