Zhou Mi, Yao Weixin
Department of Statistics, University of California, Riverside, CA, USA.
J Appl Stat. 2021 Nov 10;50(3):744-760. doi: 10.1080/02664763.2021.1999398. eCollection 2023.
Causal inference under the potential outcome framework relies on the strongly ignorable treatment assumption. This assumption is usually questionable in observational studies, and the unmeasured confounding is one of the fundamental challenges in causal inference. To this end, we propose a new sensitivity analysis method to evaluate the impact of the unmeasured confounder by leveraging ideas of doubly robust estimators, the exponential tilt method, and the super learner algorithm. Compared to other existing methods of sensitivity analysis that parameterize the unmeasured confounder as a latent variable in the working models, the exponential tilting method does not impose any restrictions on the structure or models of the unmeasured confounders. In addition, in order to reduce the modeling bias of traditional parametric methods, we propose incorporating the super learner machine learning algorithm to perform nonparametric model estimation and the corresponding sensitivity analysis. Furthermore, most existing sensitivity analysis methods require multivariate sensitivity parameters, which make its choice difficult and subjective in practice. In comparison, the new method has a univariate sensitivity parameter with a nice and simple interpretation of log-odds ratios for binary outcomes, which makes its choice and the application of the new sensitivity analysis method very easy for practitioners.
潜在结果框架下的因果推断依赖于强可忽略治疗假设。在观察性研究中，这一假设通常存在问题，而未测量的混杂因素是因果推断中的基本挑战之一。为此，我们提出一种新的敏感性分析方法，通过利用双重稳健估计量、指数倾斜法和超级学习算法的思想来评估未测量混杂因素的影响。与其他现有的敏感性分析方法相比，这些方法将未测量的混杂因素参数化为工作模型中的潜在变量，而指数倾斜法对未测量混杂因素的结构或模型不施加任何限制。此外，为了减少传统参数方法的建模偏差，我们建议纳入超级学习机器学习算法来进行非参数模型估计和相应的敏感性分析。此外，大多数现有的敏感性分析方法需要多变量敏感性参数，这使得其在实践中的选择既困难又主观。相比之下，新方法有一个单变量敏感性参数，对于二元结果具有对数优势比的简洁解释，这使得从业者对新敏感性分析方法的选择和应用非常容易。
