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首页> 外文期刊>International Journal of Statistics and Probability >Causal Subclassification Tree Algorithm and Robust Causal Effect Estimation via Subclassification
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Causal Subclassification Tree Algorithm and Robust Causal Effect Estimation via Subclassification

机译：因果子分类树算法和通过子类化的鲁棒因果效应估计

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In observational studies, the existence of confounding variables should be attended to, and propensity score weighting methods are often used to eliminate their e ects. Although many causal estimators have been proposed based on propensity scores, these estimators generally assume that the propensity scores are properly estimated. However, researchers have found that even a slight misspecification of the propensity score model can result in a bias of estimated treatment effects. Model misspecification problems may occur in practice, and hence, using a robust estimator for causal effect is recommended. One such estimator is a subclassification estimator. Wang, Zhang, Richardson, Zhou (2020) presented the conditions necessary for subclassification estimators to have $sqrt{N}$-consistency and to be asymptotically well-defined and suggested an idea how to construct subclasses.
机译：在观察性研究中，应参加混淆变量的存在，并且通常用于消除其E ECTS的倾向分数加权方法。尽管基于倾向分数提出了许多因果估计，但这些估计人员通常认为倾向评分估计。然而，研究人员发现，即使对倾向评分模型的轻微误操作甚至可能导致估计治疗效果的偏差。模型拼写错误可能在实践中发生，因此，建议使用鲁棒估算器进行因果效果。一个这样的估计器是子类化估计器。王，张，理查森，周（2020）介绍了子类化估计人为$ sqrt {n} $ - 一致性所需的条件，并呈渐近定义，并建议如何构建子类。

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