Bayesian bias modelling for causal inference in statistics

因果推断是统计学中一个蓬勃发展的创新领域，在社会科学、医学等领域有着重要的应用。这项研究计划的目标是开发新的统计方法，用于因果推断中的贝叶斯偏差分析。这项研究将建立在申请人最新进展的基础上，其中包括已在统计期刊上发表的新的因果推断方法，以及在特定领域应用领域产生影响的新的因果推断方法。短期目标是A)开发一种贝叶斯方法来探索因果推理中对多个未测量混杂因素的敏感性，以及B)开发和研究新的贝叶斯方法来估计倾向分数以控制混杂。更长期的目标包括：C)开发贝叶斯方法，在观察性研究的荟萃分析的背景下模拟偏差的异质性；D)研究贝叶斯技术在纵向研究中从不可忽视的缺失数据中建模偏差的作用。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方法的性能比较。方法论方法将包括计算机模拟实验，基于简单模型的分析结果，以及应用真实数据集时的性能研究。受训者将参与研究的所有方面，并获得应用于独特数据集的生物统计学方法方面的知识。拟议的工作将对因果推理的新方法的研究产生影响。此外，它还将影响从业者分析数据的方式。最重要的是，我的学术职位位于旧金山大学健康科学学院，我是统计和精算科学系的准成员。这一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.