Covariate Adjustment in Experiments with Matched Pairs
配对随机实验中的协变量调整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：摘要研究配对随机实验中平均处理效应的协变量调整与推断精度。
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…
配对随机实验中的协变量调整
- 研究问题
- 研究按基线协变量配对并在每对内随机分配处理的实验中，如何利用额外基线协变量提高平均处理效应估计的精度。
- 方法与思路
- 作者基于双重稳健矩条件分析有限维线性调整、配对固定效应和两种 LASSO 高维调整，并结合模拟及实验数据再分析。
- 材料陈述的贡献
- 摘要称普通线性调整未必改善精度，处理交互也不改变精度；配对固定效应可确保增益，两种 LASSO 调整在所述条件下也有改进。
- 阅读关注点
- 应核对精度比较采用的渐近框架、配对固定效应的最优性范围，以及 LASSO 调整获得精度增益和非参数最优性所需条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未列明正则条件与有限样本结果，所称最优性不能脱离调整类别解释。
原文摘要
This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to adjust for observed, baseline covariates to gain further precision. By a "matched pairs" design, we mean that units are sampled i.i.d. from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Importantly, we presume that not all observed, baseline covariates are used in determining treatment assignment. We study a broad class of estimators based on a "doubly robust" moment condition that permits us to study estimators with both finite-dimensional and high-dimensional forms of covariate adjustment. We find that estimators with finite-dimensional, linear adjustments need not lead to improvements in precision relative to the unadjusted difference-in-means estimator. This phenomenon persists even if the adjustments are interacted with treatment; in fact, doing so leads to no changes in precision. However, gains in precision can be ensured by including fixed effects for each of the pairs. Indeed, we show that this adjustment is the "optimal" finite-dimensional, linear adjustment. We additionally study two estimators with high-dimensional forms of covariate adjustment based on the LASSO. For each such estimator, we show that it leads to improvements in precision relative to the unadjusted difference-in-means estimator and also provide conditions under which it leads to the "optimal" nonparametric, covariate adjustment. A simulation study confirms the practical relevance of our theoretical analysis, and the methods are employed to reanalyze data from an experiment using a "matched pairs" design to study the effect of macroinsurance on microenterprise.