Department of Mathematics, Syracuse University, Syracuse, NY, USA.
Center for Policy Research, Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, USA.
Biom J. 2021 Aug;63(6):1202-1222. doi: 10.1002/bimj.202000069. Epub 2021 Apr 21.
The goal of most empirical studies in social sciences and medical research is to determine whether an alteration in an intervention or a treatment will cause a change in the desired outcome response. Unlike randomized designs, establishing the causal relationship based on observational studies is a challenging problem because the ceteris paribus condition is violated. When the covariates of interest are measured with errors, evaluating the causal effects becomes a thorny issue. We propose a semiparametric method to establish the causal relationship, which yields a consistent estimator of the average causal effect. The method we proposed results in locally efficient estimators of the covariate effects. We study their theoretical properties and demonstrate their finite sample performance on simulated data. We further apply the proposed method to the Stroke Recovery in Underserved Populations (SRUP) study by the National Institute on Aging.
大多数社会科学和医学研究中的实证研究的目标是确定干预或治疗的改变是否会导致所需结果响应的变化。与随机设计不同，基于观察性研究确定因果关系是一个具有挑战性的问题，因为违反了其他条件不变的条件。当感兴趣的协变量存在测量误差时，评估因果效应就成为一个棘手的问题。我们提出了一种半参数方法来建立因果关系，该方法产生了平均因果效应的一致估计量。我们提出的方法得到了协变量效应的局部有效估计量。我们研究了它们的理论性质，并在模拟数据上展示了它们的有限样本性能。我们进一步将所提出的方法应用于美国国家老龄化研究所的服务不足人群中风恢复研究（SRUP）。
