论文标题
倾向分数模型及其在某些因果估计中的应用估算方法：审查和建议
Robust Estimating Method for Propensity Score Models and its Application to Some Causal Estimands: A review and proposal
论文作者
论文摘要
在观察性研究中，倾向评分具有估计因果影响的核心作用。由于倾向得分通常是未知的，因此通过适当的程序进行估计是必不可少的步骤。一个点指的是，如果倾向得分模型误指定，因果效应估计器可能会有一些偏差。有效的模型构建很重要。为了克服这个问题，已经提出了各种有趣的方法。在本文中，我们回顾了四种方法：使用普通的逻辑回归方法；由Imai和Ratkovic提出的CBP；由McCaffrey及其同事提出的提升推车； Liu及其同事提出的半参数策略。此外，我们提出了新颖的强大两步策略：在第一步中估算每个候选模型并将其集成在第二步中。我们通过估计Li及其同事提出的ATE和ATO来确认这些方法的性能。从模拟示例的结果来看，具有高阶平衡条件的增强的购物车和CBP具有良好的特性。 ATE和ATO的估计都具有较小的差异和偏差的绝对值。增强的购物车和CBP可用于多种估计和估计程序。
In observational study, the propensity score has the central role to estimate causal effects. Since the propensity score is usually unknown, estimating by appropriate procedures is an indispensable step. A point to note that a causal effect estimator might have some bias if a propensity score model was misspecified; valid model construction is important. To overcome the problem, a variety of interesting methods has been proposed. In this paper, we review four methods: using ordinary logistic regression approach; CBPS proposed by Imai and Ratkovic; boosted CART proposed by McCaffrey and colleagues; a semiparametric strategy proposed by Liu and colleagues. Also, we propose the novel robust two step strategy: estimating each candidate model in the first step and integrating them in the second step. We confirm the performance of these methods through simulation examples by estimating the ATE and ATO proposed by Li and colleagues. From the results of the simulation examples, the boosted CART and CBPS with higher-order balancing condition have good properties; both the estimate of the ATE and ATO has the small variance and the absolute value of bias. The boosted CART and CBPS are useful for a variety of estimands and estimating procedures.
