Mahalanobis balancing: a multivariate perspective on approximate covariate balancing
马氏平衡：近似协变量平衡的多变量视角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：摘要明确将协变量平衡加权用于因果推断，并讨论倾向得分及处理组与对照组的重叠。
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…
马氏平衡：近似协变量平衡的多变量视角
- 研究问题
- 当精确协变量平衡因重叠不足或高维而不可行、逐项阈值又难以选择时，如何控制整体不平衡并构造因果分析权重？
- 方法与思路
- 作者提出用单一阈值的二次约束控制多变量整体不平衡，给出阈值选择程序，并研究其正则化回归对偶及高维推广。
- 材料陈述的贡献
- 摘要称方法的对偶对应于基于 l₂ 范数的正则化回归，并建立与倾向得分模型的联系、渐近性质及数值比较。
- 阅读关注点
- 核对二次约束衡量何种分布差异、阈值选择依据，以及重叠不足和高维情况下的可行性、目标效应与渐近条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已通过同行评审或被期刊接收；摘要未说明具体识别假设及有限样本优势范围。
原文摘要
In the past decade, various exact balancing-based weighting methods were introduced to the causal inference literature. Exact balancing alleviates the extreme weight and model misspecification issues that may incur when one implements inverse probability weighting. It eliminates covariate imbalance by imposing balancing constraints in an optimization problem. The optimization problem can nevertheless be infeasible when there is bad overlap between the covariate distributions in the treated and control groups or when the covariates are high-dimensional. Recently, approximate balancing was proposed as an alternative balancing framework, which resolves the feasibility issue by using inequality moment constraints instead. However, it can be difficult to select the threshold parameters when the number of constraints is large. Moreover, moment constraints may not fully capture the discrepancy of covariate distributions. In this paper, we propose Mahalanobis balancing, which approximately balances covariate distributions from a multivariate perspective. We use a quadratic constraint to control overall imbalance with a single threshold parameter, which can be tuned by a simple selection procedure. We show that the dual problem of Mahalanobis balancing is an l_2 norm-based regularized regression problem, and establish interesting connection to propensity score models. We further generalize Mahalanobis balancing to the high-dimensional scenario. We derive asymptotic properties and make extensive comparisons with existing balancing methods in the numerical studies.