A New Covariate Selection Strategy for High Dimensional Data in Causal Effect Estimation with Multivariate Treatments
多变量处理因果效应估计中的高维协变量选择新策略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：摘要明确以观察性数据中的多变量处理效应和混杂变量筛选为目标，直接属于因果推断方向。
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
多变量处理因果效应估计中的高维协变量选择新策略
- 研究问题
- 在高维乃至超高维处理前协变量下，如何为多变量处理效应估计选择混杂因素和结果预测变量，以控制偏差并改善效率？
- 方法与思路
- 作者提出 DSPAL，将双重筛选获得的边际条件独立或依赖先验信息与 adaptive lasso 结合，适用于不同结果模型。
- 材料陈述的贡献
- 摘要称方法具有确定筛选、排序一致性与变量选择一致性，并在所考察模拟中较若干替代方法降低偏差和均方误差。
- 阅读关注点
- 应核对筛选与惩罚步骤、上述一致性结论的条件，以及变量选择如何衔接三维连续处理的因果效应识别与估计。
- 阅读边界
- 所给为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明识别假设、维度增长限制或比较结果数值。
原文摘要
Selection of covariates is crucial in the estimation of average treatment effects given observational data with high or even ultra-high dimensional pretreatment variables. Existing methods for this problem typically assume sparse linear models for both outcome and univariate treatment, and cannot handle situations with ultra-high dimensional covariates. In this paper, we propose a new covariate selection strategy called double screening prior adaptive lasso (DSPAL) to select confounders and predictors of the outcome for multivariate treatments, which combines the adaptive lasso method with the marginal conditional (in)dependence prior information to select target covariates, in order to eliminate confounding bias and improve statistical efficiency. The distinctive features of our proposal are that it can be applied to high-dimensional or even ultra-high dimensional covariates for multivariate treatments, and can deal with the cases of both parametric and nonparametric outcome models, which makes it more robust compared to other methods. Our theoretical analyses show that the proposed procedure enjoys the sure screening property, the ranking consistency property and the variable selection consistency. Through a simulation study, we demonstrate that the proposed approach selects all confounders and predictors consistently and estimates the multivariate treatment effects with smaller bias and mean squared error compared to several alternatives under various scenarios. In real data analysis, the method is applied to estimate the causal effect of a three-dimensional continuous environmental treatment on cholesterol level and enlightening results are obtained.