IDENTIFYING CAUSAL MECHANISMS (PRIMARILY) BASED ON INVERSE PROBABILITY WEIGHTING
主要基于逆概率加权识别因果机制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。
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：摘要明确研究二元处理的直接与间接因果效应，并以逆概率加权识别中介机制。
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…
关注这篇论文的作者
主要基于逆概率加权识别因果机制
- 研究问题
- 在基于可观测变量选择的情形下，如何识别二元处理经中介变量产生的平均间接效应和直接效应，尤其如何处理非随机的中介选择？
- 方法与思路
- 作者主要使用给定中介变量和可观测混杂因素的条件处理倾向进行逆概率加权，并区分混杂因素是否受处理影响的情形。
- 材料陈述的贡献
- 摘要称加权形式及适用范围取决于混杂因素是否受处理影响，并以模拟和美国 Job Corps 项目的就业中介及健康效应应用展示结果。
- 阅读关注点
- 应核对不同混杂结构下的识别假设、条件倾向与效应定义，以及随机处理为何不足以识别中介机制，并查看应用中的变量时间顺序。
- 阅读边界
- 仅依据所给 OpenAlex 摘要；完整识别条件、支持要求、权重估计与推断细节未说明，不能据此判断实际应用满足全部假设。
原文摘要
This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting; i.e. we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the (unmediated) direct effect. Even under random treatment assignment, subsequent selection into the mediator is generally non-random such that causal mechanisms are only identified when controlling for confounders of the mediator and the outcome. To tackle this issue, units are weighted by the inverse of their conditional treatment propensity given the mediator and observed confounders. We show that the form and applicability of weighting depend on whether some confounders are themselves influenced by the treatment or not. A simulation study gives the intuition for these results and an empirical application to the direct and indirect health effects (through employment) of the US Job Corps program is also provided. Copyright © 2013 John Wiley & Sons, Ltd.