Estimation Strategies for Causal Decomposition Analysis with Allowability Specifications
考虑可允许性设定的因果分解分析估计策略AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确以潜在结果和假想干预研究群体差距，直接属于因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
考虑可允许性设定的因果分解分析估计策略
- 研究问题
- 如何估计假想干预对群体差距的影响，同时区分哪些协变量可公平地用于定义结果差距与干预，并处理由此产生的估计困难？
- 方法与思路
- 作者在潜在结果框架下梳理估计策略，提出避免密度建模的桥接估计量、多重稳健的序贯加权回归，以及密度和权重诊断。
- 材料陈述的贡献
- 摘要称，新估计量解决若干密度建模困难，并形式化建立模型误设下的稳健性；作者通过模拟和高血压差距应用展示方法。
- 阅读关注点
- 应核对可允许协变量如何进入目标参数、因果识别条件、多重稳健性的模型组合，以及桥接估计和诊断的具体实现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列出完整识别条件、稳健性定理和模拟数值，不能判断实际优势。
原文摘要
Causal decomposition analysis (CDA) is an approach for modeling the impact of hypothetical interventions to reduce disparities. It is useful for identifying foci that future interventions, including multilevel and multimodal interventions, could focus on to reduce disparities. Based within the potential outcomes framework, CDA has a causal interpretation when the identifying assumptions are met. CDA also allows an analyst to consider which covariates are allowable (i.e., fair) for defining the disparity in the outcome and in the point of intervention, so that its interpretation is also meaningful. While the incorporation of causal inference and allowability promotes robustness, transparency, and dialogue in disparities research, it can lead to challenges in estimation such as the need to correctly model densities. Also, how CDA differs from commonly used statistical decomposition estimators from the econometrics literature may not be clear, which may limit its uptake. To address these challenges, we provide a tour of estimation strategies for CDA, reviewing existing proposals and introducing novel estimators that overcome key estimation challenges. Among them we introduce what we call "bridging" estimators that avoid modeling any density, and sequential weighted regression estimators that are multiply robust. Additionally, we provide diagnostics to assess the quality of the nuisance density models and weighting functions they rely on. We formally establish the estimators' robustness to model mis-specification, demonstrate their performance through a simulation study based on real data, and apply them to study disparities in uncontrolled hypertension using electronic health records in a large healthcare system.