论文标题
贝叶斯因果推断具有一些无效的仪器变量
Bayesian causal inference with some invalid instrumental variables
论文作者
论文摘要
在观察性研究中，仪器变量估计值大大用于识别因果效应。仪器变量估计器保持一致的关键条件之一是排除限制，这表明仪器仅通过关注的曝光变量影响兴趣的结果。我们提出了一种无似然的贝叶斯方法，以对因果效应进行一致的推论，而这些仪器以某种方式违反了排除限制条件。建立了所提出的贝叶斯估计量的渐近特性，包括一致性和正态性。一项模拟研究表明，提出的贝叶斯方法产生一致的点估计器和有效的可靠间隔，并具有适用于高斯和非高斯数据的正确覆盖率，并具有一些无效的仪器。我们还通过实际数据应用程序演示了提出的方法。
In observational studies, instrumental variables estimation is greatly utilized to identify causal effects. One of the key conditions for the instrumental variables estimator to be consistent is the exclusion restriction, which indicates that instruments affect the outcome of interest only via the exposure variable of interest. We propose a likelihood-free Bayesian approach to make consistent inferences about the causal effect when there are some invalid instruments in a way that they violate the exclusion restriction condition. Asymptotic properties of the proposed Bayes estimator, including consistency and normality, are established. A simulation study demonstrates that the proposed Bayesian method produces consistent point estimators and valid credible intervals with correct coverage rates for Gaussian and non-Gaussian data with some invalid instruments. We also demonstrate the proposed method through the real data application.
