Augmented balancing weights as linear regression
将增广平衡权重表示为线性回归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：摘要研究结合结果模型与协变量平衡权重的双重稳健估计，并以倾向得分加权为相关背景。
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…
将增广平衡权重表示为线性回归
- 研究问题
- 结合结果模型与协变量平衡权重的增广估计量，何时能化为单一线性回归？研究进一步考察正则化选择如何改变这种表示。
- 方法与思路
- 作者在结果和权重模型均对某组基函数线性时推导回归等价表示，并分别分析岭回归、核岭回归与 lasso 权重模型。
- 材料陈述的贡献
- 摘要称，部分正则化选择使估计量退化为 OLS；双岭模型等价于欠平滑岭回归，而 lasso 情形展示闭式特例和双重选择性质。
- 阅读关注点
- 应核对等价关系对应的估计目标、基函数与样本使用条件，以及正则化参数映射、无限维情况和有限样本等价对渐近分析的意义。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未核实。摘要未提供完整条件和收敛率结果，回归等价或退化为 OLS 本身不保证因果识别或效率。
原文摘要
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine learning estimators combine outcome modeling with balancing weights - weights that achieve covariate balance directly in lieu of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients that combine the coefficients from the original outcome model and coefficients from an unpenalized ordinary least squares (OLS) fit on the same data. We see that, under certain choices of regularization parameters, the augmented estimator often collapses to the OLS estimator alone; this occurs for example in a re-analysis of the Lalonde 1986 dataset. We then extend these results to specific choices of outcome and weighting models. We first show that the augmented estimator that uses (kernel) ridge regression for both outcome and weighting models is equivalent to a single, undersmoothed (kernel) ridge regression. This holds numerically in finite samples and lays the groundwork for a novel analysis of undersmoothing and asymptotic rates of convergence. When the weighting model is instead lasso-penalized regression, we give closed-form expressions for special cases and demonstrate a ``double selection'' property. Our framework opens the black box on this increasingly popular class of estimators, bridges the gap between existing results on the semiparametric efficiency of undersmoothed and doubly robust estimators, and provides new insights into the performance of augmented balancing weights.