Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data
高维数据下正则化校准估计的处理效应模型辅助推断AI 解读 ↓
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高维数据下正则化校准估计的处理效应模型辅助推断
- 研究问题
- 使用大量协变量调整潜在混杂时，如何在倾向得分或结果模型存在误设的情况下，估计平均处理效应并构造有效区间？
- 方法与思路
- 作者采用带 Lasso 惩罚和特定损失函数的正则化校准估计，拟合倾向得分与结果模型，并进行高维理论和模拟分析。
- 材料陈述的贡献
- 摘要称，点估计双重稳健，区间在倾向模型正确时有效；结果模型为线性时区间也双重稳健，并提供 RCAL 实现。
- 阅读关注点
- 阅读时应核对因果识别与稀疏条件、校准损失的作用，以及点估计和区间的稳健性差别，特别是线性结果模型的限定。
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- OpenAlex 索引摘要未注明稿件版本；未展开识别和稀疏条件，模拟与应用中的优势不能推广为所有高维设定均更优。
原文摘要
Consider the problem of estimating average treatment effects when a large number of covariates are used to adjust for possible confounding through outcome regression and propensity score models. We develop new methods and theory to obtain not only doubly robust point estimators for average treatment effects, which remain consistent if either the propensity score model or the outcome regression model is correctly specified, but also model-assisted confidence intervals, which are valid when the propensity score model is correctly specified but the outcome model may be misspecified. With a linear outcome model, the confidence intervals are doubly robust, that is, being also valid when the outcome model is correctly specified but the propensity score model may be misspecified. Our methods involve regularized calibrated estimators with Lasso penalties but carefully chosen loss functions, for fitting propensity score and outcome regression models. We provide high-dimensional analysis to establish the desired properties of our methods under comparable sparsity conditions to previous results, which give valid confidence intervals when both the propensity score and outcome models are correctly specified. We present simulation studies and an empirical application which demonstrate advantages of the proposed methods compared with related methods based on regularized maximum likelihood estimation. The methods are implemented in the R package $\mathtt{RCAL}$.