Improving Precision of RCT-Based CATE Estimation using Data Borrowing with Double Calibration
以双重校准借用观察数据提升随机试验 CATE 估计精度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：摘要直接研究随机试验总体的条件平均治疗效应，以及混杂观察数据的辅助使用和一致性，明确属于因果推断方向。
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…
以双重校准借用观察数据提升随机试验 CATE 估计精度
- 研究问题
- 如何利用大型观察研究提高随机试验中条件平均治疗效应的估计精度，并在观察研究存在混杂或总体分布差异时保留试验目标？
- 方法与思路
- 作者以反事实均值作为增广函数，提出 R-OSCAR 两阶段估计器，将观察数据预测校准至试验总体并修正残余偏差，辅以交叉拟合诊断。
- 材料陈述的贡献
- 摘要称，文章给出误差界及效率改善条件；模拟中异质效应检出所需试验样本量最多减少 75%，并用两项案例展示借用与诊断。
- 阅读关注点
- 应核对一致性与无偏性的具体含义、结果均值差异的低复杂度条件，以及诊断能支持何种借用结论；案例还需检查总体覆盖与重叠。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列出完整条件，最多减少 75% 属于特定模拟结果，不能视为普遍收益。
原文摘要
Understanding how treatment effects vary across patient characteristics is essential for personalized medicine, yet randomized controlled trials (RCTs) are often underpowered to detect heterogeneous treatment effects (HTEs). We propose a framework that improves the efficiency of conditional average treatment effect (CATE) estimation in RCTs by leveraging large observational studies (OS) while preserving RCT unbiasedness. Framing CATE estimation as a supervised learning problem, we show that estimation variance is minimized using the counterfactual mean outcome (CMO) as an augmentation function. We derive finite-sample error bounds and give conditions under which OS data improves CMO estimation, and thus CATE efficiency, even under confounding in the OS or outcome distribution shift between populations. We introduce R-OSCAR (Robust Observational Studies for CMO-Augmented RCT), a two-stage estimator that calibrates OS outcome predictions to the RCT population and corrects residual bias through regularized regression. For any OS-derived nuisance, R-OSCAR is consistent for the RCT-population CATE, and is efficient relative to RCT-only estimators when the RCT-OS outcome mean discrepancy is estimable from the RCT at lower complexity than the full RCT outcome model. A cross-fitted RCT diagnostic determines, from observable data alone, whether borrowing from a given OS is supported. Simulations show R-OSCAR can reduce the RCT sample size needed for HTE detection by up to 75%, while remaining robust to misspecification. We validate on two case studies: a semi-synthetic analysis of the Tennessee STAR study with constructed observational confounding, and the Greenlight Plus pediatric-obesity trial linked with external electronic-health-record controls, where borrowing improves control-arm estimation for small trials and the diagnostic certifies it only where the records cover the trial population.