Non-Parametric Bayesian Methods for Causal Inference

描述(由申请人提供)：该项目的总体目标是开发贝叶斯非参数(BNP)方法，用于从复杂数据中估计因果效应。我们专注于两个广泛的领域：时变治疗的生存分析和调解。对于生存结果，我们发展了从结构嵌套失效时间模型估计因果参数的BNP方法，对于离散和连续时间问题都是如此。基于似然的方法一般不适用于这些模型，因为它需要许多参数建模假设。我们的BNP方法应该比参数模型提供更大的灵活性，同时保持计算优势。我们将为广泛的场景(例如，多项式或连续值处理、已知或未知的审查时间)开发这些方法，并开发与不可检验的假设相关的敏感性分析方法和信息先验。对于因果调解分析，我们将以多种方式扩展我们之前的工作。最重要的是，我们将通过在模型中包含协变量来削弱识别假设。此外，我们将推广到更广泛的结果和调解类型(例如纵向或多个调解人)。我们还将开发在具有调解功能的环境中处理不可忽略的辍学的方法。我们的方法有广泛的应用，我们将利用它们从几个行为干预试验和几类抗逆转录病毒药物的肝脏安全性研究中得出新的临床推断。

DESCRIPTION (provided by applicant): The overarching goal of this project is to develop Bayesian non-parametric (BNP) methods for estimating causal effects from complex data. We focus on two broad areas: survival analysis with time-varying treatments and mediation. For survival outcomes, we develop BNP methods for estimating causal parameters from structural nested failure time models, both for discrete and continuous-time problems. Likelihood-based methods have generally not been implemented for these models, because it would require many parametric modeling assumptions. Our BNP approach should provide greater flexibility than parametric models, while maintaining computational advantages. We will develop these methods for a wide array of scenarios (e.g., multinomial or continuous-valued treatment, known or unknown censoring times) and develop sensitivity analysis methods and informative priors related to untestable assumptions. For causal mediation analysis, we will extend our previous work in a variety of ways. Most importantly, we will weaken identifying assumptions with the inclusion of covariates in the models. In addition, we will generalize to a wider variety of outcomes and types of mediation (e.g. longitudinal or multiple mediators). We will also develop methods for handling non-ignorable dropout in settings with mediation. Our methods have broad applications, and we will utilize them to draw novel clinical inference from several behavioral intervention trials and from a study on the hepatic safety of classes of antiretroviral medications