Tailored inference for finite populations: conditional validity and transfer across distributions
面向有限总体的定制推断：条件有效性与跨分布迁移AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要提出一般有限总体条件推断框架，政策与治疗只是动机例子，未明确限定为某一所列细分方向。
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面向有限总体的定制推断：条件有效性与跨分布迁移
- 研究问题
- 当研究目标是具有已知固定属性的特定有限总体时，如何获得针对该总体的条件有效推断，并迁移至协变量分布不同的新总体？
- 方法与思路
- 作者利用总体属性信息构造估计量与置信区间，将固定属性总体的参数问题表述为条件分布推断，并扩展至部分属性可见情形。
- 材料陈述的贡献
- 摘要称，区间在给定属性后渐近有效，且比超总体推断区间更短；在一定条件下，也能对协变量分布不同的新总体推断。
- 阅读关注点
- 应核对固定属性与随机变化的划分、有限总体目标参数的定义，以及跨总体迁移和属性部分观测时所需的条件。
- 阅读边界
- 仅依据所给Crossref摘要；未说明条件有效性的具体假设或区间长度比较范围，跨总体有效性不能被视为无条件保证。
原文摘要
Summary Parameters of subpopulations can be more relevant than those of superpopulations. For example, a healthcare provider may be interested in the effect of a treatment plan for a specific subset of their patients; policymakers may be concerned with the impact of a policy in a particular state within a given population. In these cases, the focus is on a specific finite population, as opposed to an infinite superpopulation. Such a population can be characterized by fixing some attributes that are intrinsic to them, leaving unexplained variations like measurement error as random. Inference for a population with fixed attributes can then be modelled as inferring parameters of a conditional distribution. Accordingly, it is desirable that confidence intervals are conditionally valid for the realized population, instead of marginalizing over many possible draws of populations. We provide a statistical inference framework for parameters of finite populations with known attributes. Leveraging the attribute information, our estimators and confidence intervals closely target a specific finite population. When the data are from the population of interest, our confidence intervals attain asymptotic conditional validity, given the attributes, and are shorter than those for superpopulation inference. In addition, we develop procedures to infer parameters of new populations with differing covariate distributions; the confidence intervals are also conditionally valid for the new populations under mild conditions. Our methods extend to situations where the fixed information has a weaker structure or is only partially observed. We demonstrate the validity and applicability of our methods using simulated data and a real-word dataset for predicting car prices.