发表机构
Meta（Meta）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对代理估计存在的问题，提出Proxymate框架及Python包，通过四个级别进行代理验证和调整，提供诊断与策略，在Meta多个用例中应用，校正大量代理与主要单元比较，推动多工作流发布。
AI 中文摘要
代理结果（如短期行为信号、模型预测或替代终点）常被用于替代成熟缓慢、罕见或难以直接测量的主要结果。但对代理的有效推断不能保证对主要估计的有效推断，因为基于代理的估计可能存在难以预测的系统偏差，导致置信区间校准不当。我们提出了Proxymate，一个用于代理验证和调整的框架及开源Python包。它分为代表性、单元、估计和领域四个级别，各级别提供诊断检查和针对性调整策略。在Meta，它已被多种用例采用，评估和校正了数百万次代理与主要单元比较，促进了多个工作流的发布。
英文摘要
Proxy outcomes (such as short-term behavioral signals, model predictions, or surrogate endpoints) are frequently used in place of primary outcomes that are too slow to mature, rare, or challenging to measure directly. But valid inference on a proxy does not guarantee valid inference on the primary estimate as proxy-based estimates can be systematically biased in ways that are difficult to predict, leading to improperly calibrated confidence intervals. We present proxymate, a framework and open-source Python package for proxy validation and adjustment. proxymate organizes into four levels: The Representativity Level (population validity), the Unit Level (measurement quality), the Estimate Level (decision validity), and the Domain Level (cross-domain transportability). Within each level, proxymate provides diagnostic checks, and targeted adjustment strategies that map specific failures to appropriate corrections. At Meta, proxymate has been adopted by many different use cases, spanning experimentation, prevalence estimation, and monitoring use cases, all facing different proxy challenges (limited human review time, long maturation window of outcomes, low detectability) and showcasing the modularity of the framework. Across all products, proxymate assessed and corrected millions of proxy, primary unit comparisons. It has facilitated launches across multiple work streams including enabling quick decision making on thousands of experiments.
Comments15 pages, 2 figures
