What type of study is this?

This is a Quantitative Study study.

October 3, 2025Open Access

Beyond ATE: Multi-Criteria Design for A/B Testing

View Full Paper

Ask AI

Bookmark

Authors

This paper reports on the trade-offs between statistical accuracy and welfare loss in A/B testing, suggesting Pareto optimal solutions.

Achieving optimal post-experiment welfare while balancing statistical accuracy and social welfare loss is crucial in A/B testing.
The study establishes bounds for a multi-objective optimization problem and uses Pareto optimality to inform experimental design.
Differentially private algorithms are developed to ensure robust treatment allocation while maintaining privacy and achieving desired outcomes.
The findings indicate that accurate estimation of treatment effects can coexist with significant welfare improvements within A/B tests.

Li et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark