Robust Bayesian Semiparametric Inference of Heterogeneous Causal Effects in Observational Studies

一项至关重要的科学使命是将大量数据转化为可操作的知识，这主要集中在理解因果关系上。 除了描述性和预测性分析之外，因果推理已经成为数据科学的三大主要任务之一。 该研究项目旨在缩小异质因果效应估计方面的现有差距，并将为分析大量观测数据提供更多的统计工具。 它将把传统的因果推理统计方法与快速发展的机器学习技术相结合，为研究人员和政策制定者提供强大的方法工具，以更好地评估干预措施的影响，从而优化决策。 统计学和生物统计学的博士生将参与方法的开发和实施。该项目涉及开发一系列创新的贝叶斯半参数方法，用于在大型观测数据集中存在效应异质性的情况下进行有效和稳健的因果推理。 传统的统计方法与随机实验有很强的联系，随机实验很容易解释因果关系，但可能会影响效率。 此外，最近开发的非参数回归和机器学习方法主要集中在结果建模和预测上，这可能会遇到混淆的问题，并且通常更难以解释。 此外，来自未测量混杂的隐藏偏倚是观察性研究中的主要问题。 用于评估隐藏偏倚的现状敏感性分析不适用于复杂的数据结构。 PI将开发一个稳健的贝叶斯半参数框架，用于将治疗分配过程纳入结局建模。 该框架可以很容易地容纳复杂的异质性效应或层次结构在大量的观测数据，充分利用专家的知识和现有的因果理论的干预可能如何工作，并有效地评估由于潜在的不可测量的混杂因素的影响。 倾向评分将通过高斯过程先验纳入潜在的结果模型，并与传统的匹配估计建立联系。 此外，不可测量的混杂因素的影响将通过贝叶斯敏感性分析进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A scientific mission of critical importance is to transform massive data into actionable knowledge, which largely centers on understanding causal relationships. Causal inference has become one of three main tasks in data science, in addition to descriptive and predictive analyses. This research project aims to close existing gaps in estimation of heterogeneous causal effects and will make more statistical tools available for analyzing massive observational data. It will blend the conventional statistical approaches to causal inference with the fast-growing machine learning techniques and provide researchers and policy makers with powerful methodological tools to better evaluate the impact of interventions and thus to optimize decision making. Doctoral students in Statistics and Biostatistics will be involved in the development and implementation of the methods.This project concerns the development of a stream of innovative Bayesian semiparametric methods for efficient and robust causal inference in the presence of effect heterogeneity in large observational datasets. Conventional statistical approaches have a strong tie to randomized experiments, which enjoy easy causal interpretation but may suffer in terms of efficiency. Moreover, recently developed nonparametric regression and machine learning methods focus primarily on outcome modelling and prediction, which may encounter troubles from confounding and are often more difficult to interpret. Furthermore, hidden bias from unmeasured confounding is a major concern in observational studies. The status quo sensitivity analysis for assessing hidden bias does not accommodate complex data structures. The PIs will develop a robust Bayesian semiparametric framework for incorporating the treatment assignment process into the outcome modelling. The framework can easily accommodate complex heterogenous effects or hierarchical structures in massive observational data, adequately take advantage of experts’ knowledge and existing causal theory on how the intervention might work, and effectively assess the impact due to potential unmeasured confounders. Propensity scores will be incorporated in potential outcome models via Gaussian process priors and connections with the conventional matching estimators will be established. Moreover, the impact of unmeasured confounding will be assessed through Bayesian sensitivity analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.