Bayesian bias modelling for causal inference in statistics

因果推理是统计学中一个蓬勃发展的创新领域，在社会科学、医学和其他领域有着重要的应用。本研究的目的是发展新的统计方法，用于因果推断中的贝叶斯偏差分析。该研究将建立在申请人的最新进展的基础上，其中包括已在统计学期刊上发表的新的因果推理方法，此外，还对特定领域的应用领域产生了影响。短期目标是：A）开发贝叶斯方法，以探索因果推断中对多个未测量混杂因素的敏感性，以及B）开发和研究用于估计倾向评分以控制混杂因素的新型贝叶斯方法。长期目标包括C）在观察性研究的荟萃分析背景下，开发贝叶斯方法来模拟偏倚的异质性，以及D）研究贝叶斯技术在纵向研究中对不可验证的缺失数据的偏倚建模中的作用。该研究的一个关键组成部分将是研究新的贝叶斯方法与标准频率论方法相比的性能。该方法将包括计算机模拟实验，基于简单模型的分析结果，以及应用真实的数据集时的性能研究。学员将参与研究的各个方面，并获得应用于独特数据集的生物统计方法的知识。拟议的工作将影响因果推理新方法的研究。此外，它还将影响从业人员分析数据的方式。至关重要的是，我的学术任命位于SFU健康科学学院，我是统计和精算科学系的准会员。这种独特的环境是学生跨学科培训，新数据集和新统计方法灵感的来源。

Causal inference is a thriving area of innovation in statistics, with important applications in the social sciences, medicine, and other fields. The goal of this research program is to develop new statistical methodology for Bayesian bias analysis in causal inference. The research will build on the recent progress of the applicant, which includes new causal inference methods that have been published in statistics journals, and additionally, that have had an impact in domain-specific applied areas. The short-term objectives are A) To develop a Bayesian method to explore sensitivity to multiple unmeasured confounders in causal inference, and B) to develop and investigate novel Bayesian methods for estimating propensity scores to control confounding. Longer-term goals include C) the development of Bayesian approaches to model heterogeneity of bias in the context of meta-analysis of observational studies, and D) To study the role of Bayesian techniques to model bias from non-ignorable missing data in longitudinal studies. A key component of the research will be to study the performance of new Bayesian methods compared to standard frequentist approaches. The methodological approach will include computer simulation experiments, analytical results based on simple models, and a study of performance when applied real datasets. Trainees will be involved in all aspects of the research and gain knowledge in biostatistical methods applied to unique datasets. The proposed work will impact research on new methods for causal inference. Furthermore, it will impact how practitioners analyze data. Crucially, my academic appointment is located in the Faculty of Health Sciences at SFU, and I am an associate member in the Department of Statistics and Actuarial Sciences. This unique setting is a source of interdisciplinary training for students, new datasets, and inspiration for new statistical methods.