Choosing Covariate Balancing Methods for Causal Inference: Practical Insights from a Simulation Study
如何选择因果推断的协变量平衡方法：模拟研究的实践启示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：摘要直接比较观测研究中混杂调整方法对平均处理效应估计和推断的影响。
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…
如何选择因果推断的协变量平衡方法：模拟研究的实践启示
- 研究问题
- 在样本量、处理比例、混杂强度及重叠程度变化时，常见协变量加权方法如何影响处理效应估计的偏差、方差和区间覆盖？
- 方法与思路
- 作者在三十六种模拟情景中比较 IPTW、能量平衡、核最优匹配与定制损失平衡方法，结合加权回归及可用的双重稳健估计。
- 材料陈述的贡献
- 摘要称比较揭示各方法的调参与失效模式；结果回归纳入所有混杂变量时，双重稳健估计降低对权重方案的敏感性。
- 阅读关注点
- 应核对类别型协变量与重叠设置、各方法调参及推断实现差异，并区分偏差、均方根误差和覆盖率所反映的问题。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；结论受所设模拟场景约束，权重估计不确定性的区间处理仍是摘要指出的难点。
原文摘要
Background: Inverse probability of treatment weighting (IPTW) is used for confounding adjustment in observational studies. Newer weighting methods include energy balancing (EB), kernel optimal matching (KOM), and tailored-loss covariate balancing propensity scores (TLF), but practical guidance remains limited. We evaluate their performance when implemented according to published recommendations. Methods: We conducted Monte Carlo simulations across 36 scenarios varying sample size, treatment prevalence, and a complexity factor increasing confounding and reducing overlap. Data generation used predominantly categorical covariates with some correlation. Average treatment effect and average treatment effect on the treated were estimated using IPTW, EB, KOM, and TLF combined with weighted least squares and, when supported, a doubly robust (DR) estimators. Inference followed published recommendations for each method when feasible, using standard alternatives otherwise. \textsc{PROBITsim} dataset used for illustration. Results: DR reduced sensitivity to the weighting scheme with an outcome regression adjusted for all confounders, despite functional-form misspecification. EB and KOM were most reliable; EB was tuning-free but scale dependent, whereas KOM required kernel and penalty choices. IPTW was variance sensitive when treatment prevalence was far from 50\%. TLF traded lower variance for higher bias, producing an RMSE plateau and sub-nominal confidence interval coverage. \textsc{PROBITsim} results mirrored these patterns. Conclusions: Rather than identifying a best method, our findings highlight failure modes and tuning choices to monitor. When the outcome regression adjusts for all confounders, DR estimation can be dependable across weighting schemes. Incorporating weight-estimation uncertainty into confidence intervals remains a key challenge for newer approaches.