Statistical Inference Based on Randomly Generated Auxiliary Variables
基于随机生成辅助变量的统计推断AI 解读 ↓
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基于随机生成辅助变量的统计推断
- 研究问题
- 当现有辅助变量只能揭示部分偏差或关联时，能否把辅助变量视为变量总体中的选择，从而支持更广范围的统计推断？
- 方法与思路
- 作者建立辅助变量可能随机选自总体变量集合的框架，讨论总体多样性与扩散性，并用调查非响应分析说明用途。
- 材料陈述的贡献
- 摘要称该框架给出超越辅助变量自身所揭示偏差或效应的推断条件，并讨论关键总体特征的定义及估计。
- 阅读关注点
- 应核对变量总体与选择机制的定义、推断条件和关键特征如何估计，并区分非响应应用与因果推断动机。
- 阅读边界
- 仅据所给 Crossref 摘要；随机选择机制及推断条件未展开，不能据此认定框架消除了未观测混杂或非响应偏差。
原文摘要
Summary In most real life studies, auxiliary variables are available and are employed to explain and understand missing data patterns and to evaluate and control causal relationships with variables of interest. Usually their availability is assumed to be a fact, even if the variables are measured without the objectives of the study in mind. As a result, inference with missing data and causal inference require some assumptions that cannot easily be validated or checked. In this paper, a framework is constructed in which auxiliary variables are treated as a selection, possibly random, from the universe of variables on a population. This framework provides conditions to make statistical inference beyond the traces of bias or effects found by the auxiliary variables themselves. The utility of the framework is demonstrated for the analysis and reduction of non-response in surveys. However, the framework may be more generally used to understand the strength of association between variables. Important roles are played by the diversity and diffusion of the population of interest, features that are defined in the paper and the estimation of which is discussed.