Non-Parametric Bayesian Methods for Causal Inference

描述（由申请人提供）：本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。我们专注于两大领域：时变治疗的生存分析和调解。对于生存结果，我们开发了从结构嵌套失效时间模型估计因果参数的BNP方法，用于离散和连续时间问题。基于似然的方法通常没有被用于这些模型，因为它需要许多参数化建模假设。我们的BNP方法应该提供比参数模型更大的灵活性，同时保持计算优势。我们将为一系列广泛的场景（例如，多项或连续值处理，已知或未知的审查时间）开发这些方法，并开发敏感性分析方法和与不可检验假设相关的信息先验。对于因果中介分析，我们将以各种方式扩展我们以前的工作。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中介类型（例如纵向或多重中介）。我们还将开发处理调解设置中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抗逆转录病毒药物的肝脏安全性研究中得出新的临床结论

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