Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
分位数结果自适应 Lasso：分位数处理效应逆概率加权估计的协变量选择AI 解读 ↓
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分位数结果自适应 Lasso：分位数处理效应逆概率加权估计的协变量选择
- 研究问题
- 使用倾向得分加权估计分位数处理效应时，如何选择与结果均值或目标分位数相关的协变量，以改善效应估计表现？
- 方法与思路
- 作者以分位数回归作为结果模型，将其偏回归系数用于构造正则化权重，进行倾向得分模型的数据驱动协变量选择。
- 材料陈述的贡献
- 摘要称方法针对分位数处理效应选择相关协变量，并在模拟及母婴数据中显示，当协变量关联结果均值和分位数时表现良好。
- 阅读关注点
- 应核对正则化权重和调参规则、不同分位数是否分别选变量，以及选择后加权估计的重叠性、偏差和推断有效性。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或接收。摘要未给出识别与选择一致性条件，不能将其变量纳入的偏差陈述普遍化。
原文摘要
When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of covariates unrelated to the outcome in the propensity score model led to bias and large variance in the estimator of treatment effects. Many data-driven covariate selection methods have been proposed for selecting covariates related to outcomes. However, most of them assume an average treatment effect estimation and may not be designed to estimate quantile treatment effects (QTE), which is the effect of treatment on the quantiles of outcome distribution. In QTE estimation, we consider two relation types with the outcome as the expected value and quantile point. To achieve this, we propose a data-driven covariate selection method for propensity score models that allows for the selection of covariates related to the expected value and quantile of the outcome for QTE estimation. Assuming the quantile regression model as an outcome regression model, covariate selection was performed using a regularization method with the partial regression coefficients of the quantile regression model as weights. The proposed method was applied to artificial data and a dataset of mothers and children born in King County, Washington, to compare the performance of existing methods and QTE estimators. As a result, the proposed method performs well in the presence of covariates related to both the expected value and quantile of the outcome.