Covariate selection for the nonparametric estimation of an average treatment effect
平均处理效应非参数估计中的协变量选择AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论无混杂假设下平均处理效应的识别与协变量选择。
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平均处理效应非参数估计中的协变量选择
- 研究问题
- 在依赖无混杂假设的观察性研究中，如何从既有处理前协变量中选择最小控制集合，减少不必要控制造成的损失？
- 方法与思路
- 作者在Neyman–Rubin框架下刻画保持无混杂性的最小协变量集合，研究其识别，并据此提出数据驱动选择算法。
- 材料陈述的贡献
- 摘要称这些最小集合在一定假设下可被识别，并给出选择算法，为非参数处理效应估计中的控制变量取舍提供依据。
- 阅读关注点
- 核对最小集合的定义、识别假设及算法步骤，尤其检查初始协变量集合的充分性与选择后效应估计之间的关系。
- 阅读边界
- 索引摘要版本未注明，且未列出具体识别条件；数据驱动选择本身不能据此保证无混杂，也不能推断刊本变化。
原文摘要
Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance. It is tempting to include many covariates in the controlling set to try to make the assumption of an unconfounded treatment realistic. Including unnecessary covariates is suboptimal when the effect of a binary treatment is estimated nonparametrically. For instance, when using a n1/2-consistent estimator, a loss of efficiency may result from using covariates that are irrelevant for the unconfoundedness assumption. Moreover, bias may dominate the variance when many covariates are used. Embracing the Neyman–Rubin model typically used in conjunction with nonparametric estimators of treatment effects, we characterize subsets from the original reservoir of covariates that are minimal in the sense that the treatment ceases to be unconfounded given any proper subset of these minimal sets. These subsets of covariates are shown to be identified under mild assumptions. These results lead us to propose data-driven algorithms for the selection of minimal sets of covariates.