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Robust Bayesian Semiparametric Inference of Heterogeneous Causal Effects in Observational Studies

观察研究中异质因果效应的鲁棒贝叶斯半参数推理

基本信息

批准号：
2015552
负责人：
Xinyi Xu
金额：
$ 25万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015552&HistoricalAwards=false
关键词：
Robust Bayesian Semiparametric Inference Heterogeneous

项目摘要

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.

一项至关重要的科学任务是将海量数据转化为可操作的知识，这在很大程度上集中在理解因果关系上。除描述性和预测性分析外，因果推理已成为数据科学的三大主要任务之一。这一研究项目旨在弥合在异质因果效应估计方面的现有差距，并将使更多的统计工具可用于分析海量观测数据。它将把传统的因果推断统计方法与快速增长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这些方法的开发和实施。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高效和稳健的因果推断。传统的统计方法与随机实验有很强的联系，随机实验很容易进行因果解释，但在效率方面可能会受到影响。此外，最近发展起来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麻烦，而且往往更难解释。此外，来自未测量的混杂的隐藏偏差是观察性研究中的一个主要问题。用于评估隐藏偏差的现状敏感性分析不适应复杂的数据结构。绩效指标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果建模。该框架可以很容易地适应海量观测数据中的复杂异质效应或层次结构，充分利用专家关于干预可能如何发挥作用的知识和现有因果理论，并有效评估潜在的未测量混杂因素造成的影响。倾向性得分将通过高斯过程先验被纳入潜在结果模型中，并将与传统匹配估计器建立联系。此外，未测量的混淆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，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression

贝叶斯限制似然方法：贝叶斯回归中统计量不足的条件

DOI：
10.1214/21-ba1257
发表时间：
2021
期刊：
Bayesian Analysis
影响因子：
4.4
作者：
Lewis, John R.;MacEachern, Steven N.;Lee, Yoonkyung
通讯作者：
Lee, Yoonkyung

Stratified Restricted Mean Survival Time Model for Marginal Causal Effect in Observational Survival Data.

DOI：
10.1016/j.annepidem.2021.09.016
发表时间：
2021-12
期刊：
Annals of epidemiology
影响因子：
5.6
作者：
Ni A;Lin Z;Lu B
通讯作者：
Lu B

Analysis of combined probability and nonprobability samples: a simulation evaluation and application to a teen smoking behavior survey

概率和非概率组合样本分析：模拟评估及其在青少年吸烟行为调查中的应用

DOI：
10.1080/03610918.2022.2102181
发表时间：
2022
期刊：
Communications in Statistics - Simulation and Computation
影响因子：
0
作者：
Xi, Wenna;Hinton, Alice;Lu, Bo;Krotki, Karol;Keller-Hamilton, Brittney;Ferketich, Amy;Sukasih, Amang
通讯作者：
Sukasih, Amang

Bridging the design and modeling of causal inference: A Bayesian nonparametric perspective

连接因果推理的设计和建模：贝叶斯非参数视角