Robust A/B Decisions

2026-09-07

AI Summary

论文针对A/B测试中决策标准不当的问题，提出了一种基于模糊厌恶的决策框架，通过评估实验结果附近分布的惩罚值来优化未来部署环境中的经济回报。

Abstract

A/B tests are standard in firm decision making. In the standard pipeline, experimental data is converted to a deployment decision by applying a t-test of the difference in means (the lift) and deploying the treatment if lift is positive and statistically significant. This common workflow answers the wrong question. We argue that firms need a decision rule for economic payoffs in the future deployment environment, not a test of equality in the experimental sample. We develop an ambiguity-averse decision framework in which each arm is evaluated by its ambiguity-penalized value over distributions close to the experimental outcome distribution. The resulting rule has a simple closed form thanks to the Donsker-Varadhan representation and it requires only the outcome data from a standard A/B test plus one interpretable parameter governing trust in the experiment. Our rule is thus no more difficult to implement than a t-test. A mean-variance approximation shows how the rule penalizes variability, while a connection to utility maximization shows it to be a certainty equivalent. We are able to perform a real-world evaluation of our proposed rule in the context of digital marketing using an archive of 552 advertising experiments from an anonymous US-based online platform. The proposed rule substantially reduces regret relative to conventional hypothesis testing. The results show that economically conservative, distribution-aware deployment rules can outperform statistical-significance rules in digital experimentation.

Problem

• A/B Tests
• Economic Payoffs
• Deployment Decisions
• Statistical Significance

Innovation

• ambiguity-averse decision framework
• Donsker-Varadhan representation
• economic payoffs
• certainty equivalent
• regret reduction