SLOPE 与面向泛化的稳健研究设计
SLOPE and Designing Robust Studies for Generalization
AI总结：
本文提出 SLOPE 度量，用于量化条件可交换性局部违背的敏感性，帮助选择稳健人群或估计量，并指导泛化任务的稳健研究设计。
摘要：
泛化中的一个常见任务是基于现有源人群的数据来了解新的目标人群。该任务依赖于条件可交换性，即假设源人群与目标人群之间的差异完全由这两个人群的可观测特征所捕获。不幸的是，由于源人群与目标人群之间存在不可观测的差异，这一假设在实践中往往难以成立。更糟的是，该假设无法用数据加以验证，因此需要稳健的数据收集过程和 inherently 对假设违背不太敏感的研究设计。在本文中，我们提出 SLOPE（可交换性局部扰动的敏感性），这是一种简单、直观且新颖的度量，用于量化对条件可交换性局部违背的敏感性。SLOPE 结合了因果推断中敏感性分析的思想和 Hampel（1974）提出的基于导数的稳健性度量。在其他性质中，SLOPE 可帮助研究者选择（a）稳健的源人群或目标人群，或（b）稳健的估计量。此外，我们展示了 SLOPE 与影响函数之间的解析关系，研究者可利用该关系从影响函数推导出 SLOPE。最后，我们重新分析了一项跨国随机实验，并说明 SLOPE 在为泛化提供稳健研究设计方面的作用。
英文摘要：
A popular task in generalization is to learn about a new, target population based on data from an existing, source population. This task relies on conditional exchangeability, which asserts that differences between the source and target populations are fully captured by observable characteristics of the two populations. Unfortunately, this assumption is often untenable in practice due to unobservable differences between the source and target populations. Worse, the assumption cannot be verified with data, warranting the need for robust data collection processes and study designs that are inherently less sensitive to violation of the assumption. In this paper, we propose SLOPE (Sensitivity of LOcal Perturbations from Exchangeability), a simple, intuitive, and novel measure that quantifies the sensitivity to local violation of conditional exchangeability. SLOPE combines ideas from sensitivity analysis in causal inference and derivative-based measure of robustness from Hampel (1974). Among other properties, SLOPE can help investigators to choose (a) a robust source or target population or (b) a robust estimand. Also, we show an analytic relationship between SLOPE and influence functions, which investigators can use to derive SLOPE given an influence function. We conclude with a re-analysis of a multi-national randomized experiment and illustrate the role of SLOPE in informing robust study designs for generalization.
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