High-dimensional model-assisted inference for treatment effects with multi-valued treatments
多值处理效应的高维模型辅助推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究多值处理的平均处理效应、倾向得分与增广逆概率加权推断。
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多值处理效应的高维模型辅助推断
- 研究问题
- 在高维协变量和多值处理下，如何估计平均处理效应，并在工作模型可能误设时构造有效置信区间？
- 方法与思路
- 作者提出带稀疏惩罚的校准估计，以协变量平衡设计倾向得分损失，并允许结果回归使用不同系数估计副本。
- 材料陈述的贡献
- 摘要称，方法推广了增广逆概率加权估计，提供组Lasso计算算法和高维分析，并用于孕妇吸烟与出生体重研究。
- 阅读关注点
- 应核对模型误设下有效推断所需的条件、稀疏性要求，以及结果回归系数副本如何保证恰好识别。
- 阅读边界
- 这是arXiv预印本，同行评审及接收未核验。摘要未说明具体识别假设或允许误设的范围，不能推断任意模型误设均可处理。
原文摘要
Consider estimation of average treatment effects with multi-valued treatments using augmented inverse probability weighted (IPW) estimators, depending on outcome regression and propensity score models in high-dimensional settings. These regression models are often fitted by regularized likelihood-based estimation, while ignoring how the fitted functions are used in the subsequent inference about the treatment parameters. Such separate estimation can be associated with known difficulties in existing methods. We develop regularized calibrated estimation for fitting propensity score and outcome regression models, where sparsity-including penalties are employed to facilitate variable selection but the loss functions are carefully chosen such that valid confidence intervals can be obtained under possible model misspecification. Unlike in the case of binary treatments, the usual augmented IPW estimator is generalized by allowing different copies of coefficient estimators in outcome regression to ensure just-identification. For propensity score estimation, the new loss function and estimating functions are directly tied to achieving covariate balance between weighted treatment groups. We develop practical numerical algorithms for computing the regularized calibrated estimators with group Lasso by innovatively exploiting Fisher scoring, and provide rigorous high-dimensional analysis for the resulting augmented IPW estimators under suitable sparsity conditions, while tackling technical issues absent or overlooked in previous analyses. We present simulation studies and an empirical application to estimate the effects of maternal smoking on birth weights. The proposed methods are implemented in the R package mRCAL.