On the Nature and Discovery of Structure
论结构的本质与发现AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论利用非实验数据估计实验所揭示的决策相关效应，直接对应因果效应识别与估计。
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论结构的本质与发现
- 研究问题
- 在什么条件下，非实验数据能够一致估计实验所揭示且与决策有关的效应，目标效应变量与辅助变量应如何区分？
- 方法与思路
- 作者延伸实验原则，区分目标效应对应的因素变量与用于一致性、效率的伴随变量，讨论变量关系及遗漏的后果。
- 材料陈述的贡献
- 摘要称这种区分决定变量纳入规则，并据此批评若干常见一致性条件及外生、预定变量区分的使用方式。
- 阅读关注点
- 应核对因素变量和伴随变量的正式定义、允许的因果方向，以及遗漏变量、共线性和预定变量相关论断的前提。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；具体模型与证明未提供，不能将摘要中的强论断直接当作通用变量选择规则。
原文摘要
Extending principles of experimentation, we discuss conditions under which nonexperimental data allow consistent estimation of effects of the kind revealed by experimentation and relevant to decisions. We show how implications of these conditions are often overlooked and how failure to distinguish between “factors” and “concomitants” makes almost anything said about a model ambiguous if not wrong. The effects to be estimated dictate the factors to be included; consistency and efficiency determine the concomitants, whose effects are not to be estimated. Concomitants may affect but must not be affected by the factors. Effects of excluded variables on an included variable may cause inconsistency if the included variable is a factor, can only reduce inconsistency if the included variable is a concomitant. Exclusion of a variable because it is highly correlated with another may sometimes be legitimate if both variables are concomitants, never if either is a factor. A condition for consistent estimation stated in virtually every book on econometrics is meaningless in one common form, impossible to satisfy in another. The distinction between exogenous and other predetermined variables is irrelevant to consistent estimation of the effects of predetermined variables. The relation of predetermined to excluded variables is irrelevant to consistent estimation of the effects of endogenous variables.