Non-Parametric Bayesian Methods for Causal Inference

项目总结 这个项目的主要目标是开发贝叶斯非参数(BNP)方法来估计因果关系 复杂的数据。我们专注于两个广泛的领域：时变治疗的生存分析和调解。为了生存 结果，我们开发了BNP方法来估计结构嵌套故障时间模型的因果参数，这两个方法都是 离散和连续时间问题。基于似然的方法通常没有被实现用于这些模型， 因为这将需要许多参数建模假设。我们的BNP方法应该提供更大的灵活性 而不是参数模型，同时保持了计算优势。我们将开发这些方法，用于广泛的 情景(例如，多项式或连续值处理、已知或未知的审查时间)和发展敏感度 与不可检验的假设相关的分析方法和信息丰富的先例。对于因果调解分析，我们将扩展 我们以前的工作有很多种方式。最重要的是，我们将通过包含来弱化识别假设 模型中的协变量。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)。我们还将开发处理不可忽略的辍学的方法 调解。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，以及一项关于各类抗逆转录病毒药物对肝脏安全性的研究。

PROJECT SUMMARY 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.