A Two-Stage Interpretable Matching Framework for Causal Inference
用于因果推断的两阶段可解释匹配框架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：摘要直接以观察性数据匹配控制混杂，并估计条件平均处理效应，属于明确的因果推断方法研究。
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
用于因果推断的两阶段可解释匹配框架
- 研究问题
- 在观察性数据中，如何构造透明且可解释的处理组与对照组匹配，改善协变量重叠并用于条件平均处理效应估计？
- 方法与思路
- 作者先对全部协变量精确匹配，再逐步移除最不显著的混杂变量重新匹配，并为移除变量学习距离衡量层内接近程度。
- 材料陈述的贡献
- 摘要称模拟和 CDC 医疗数据应用显示，该框架改善条件平均处理效应估计及多变量重叠，并能够处理高维数据。
- 阅读关注点
- 应核对混杂变量重要性的定义、变量移除后的残余混杂，以及匹配后的目标人群、效应估计和不确定性计算。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；匹配或重叠改善本身不保证无偏，摘要未说明完整识别条件及比较数值。
原文摘要
Matching in causal inference from observational data aims to construct treatment and control groups with similar distributions of covariates, thereby reducing confounding and ensuring an unbiased estimation of treatment effects. This matched sample closely mimics a randomized controlled trial (RCT), thus improving the quality of causal estimates. We introduce a novel Two-stage Interpretable Matching (TIM) framework for transparent and interpretable covariate matching. In the first stage, we perform exact matching across all available covariates. For treatment and control units without an exact match in the first stage, we proceed to the second stage. Here, we iteratively refine the matching process by removing the least significant confounder in each iteration and attempting exact matching on the remaining covariates. We learn a distance metric for the dropped covariates to quantify closeness to the treatment unit(s) within the corresponding strata. We used these high- quality matches to estimate the conditional average treatment effects (CATEs). To validate TIM, we conducted experiments on synthetic datasets with varying association structures and correlations. We assessed its performance by measuring bias in CATE estimation and evaluating multivariate overlap between treatment and control groups before and after matching. Additionally, we apply TIM to a real-world healthcare dataset from the Centers for Disease Control and Prevention (CDC) to estimate the causal effect of high cholesterol on diabetes. Our results demonstrate that TIM improves CATE estimates, increases multivariate overlap, and scales effectively to high-dimensional data, making it a robust tool for causal inference in observational data.