Ranking by Lifts: A Cost-Benefit Approach to Large-Scale A/B Tests

• URL: http://arxiv.org/abs/2407.01036v3
• Date: Wed, 20 Aug 2025 11:28:11 GMT
• Title: Ranking by Lifts: A Cost-Benefit Approach to Large-Scale A/B Tests
• Authors: Pallavi Basu, Ron Berman,
• Abstract summary: A/B testing is a core tool for decision-making in business experimentation, particularly in digital platforms and marketplaces.<n>This paper develops a decision-theoretic framework for maximizing expected profit subject to a constraint on the cost-weighted false discovery rate (FDR)<n>We propose an empirical Bayes approach that uses a greedy knapsack algorithm to rank experiments based on the ratio of expected lift to cost, incorporating the local false discovery rate (lfdr) as a key statistic.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A/B testing is a core tool for decision-making in business experimentation, particularly in digital platforms and marketplaces. Practitioners often prioritize lift in performance metrics while seeking to control the costs of false discoveries. This paper develops a decision-theoretic framework for maximizing expected profit subject to a constraint on the cost-weighted false discovery rate (FDR). We propose an empirical Bayes approach that uses a greedy knapsack algorithm to rank experiments based on the ratio of expected lift to cost, incorporating the local false discovery rate (lfdr) as a key statistic. The resulting oracle rule is valid and rank-optimal. In large-scale settings, we establish the asymptotic validity of a data-driven implementation and demonstrate superior finite-sample performance over existing FDR-controlling methods. An application to A/B tests run on the Optimizely platform highlights the business value of the approach.

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