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Bayesian Non-Parametric Test for Conditional Independence

条件独立性的贝叶斯非参数检验

基本信息

批准号：
EP/R013519/1
负责人：
Sarah Filippi
金额：
$ 12.84万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR013519%2F1
关键词：
Bayesian Non Parametric Test Conditional

项目摘要

Conditional independence testing is at the core of modern causal discovery, which is itself of paramount importance throughout the sciences and in Machine Learning. In particular, the discovery of causal relationships is fundamental in public health and epidemiology, in life and earth sciences, in sociological studies and in econometrics. The traditional approach for such causal discovery based on randomised trials is impossible and even unethical in some of the most important applications. For example the investigation of the impact of environmental factors, such as smoking or exposure to specific chemicals, on patient health cannot be studied by randomly allocating patients to a group that would be exposed to a potentially very harmful factor. In such cases, the only way to identify causal links among a large set of variables is to exploit the growing abundance of observational studies. Most of the algorithmic approaches to causal inference from observational data rely on statistical tests of independence and conditional independence between variables. The most widely used existing independence and conditional independence tests in causal discovery, such as Pearson correlation and partial correlation, can only test for linear dependencies. While these approaches are very efficient if the relationships are indeed linear, they are blind to the type of intricate relationships present in the most challenging applications where dependencies are in fact highly non-linear. To correctly detect these relationships one needs to not only go beyond linearity assumptions, but also to use non-parametric approaches that make no assumption on the form of dependence between the variables.The project is concerned with developing a non-parametric statistical method to test for conditional independence in a Bayesian framework. The Bayesian setting will provide a rigorous statistical grounding and the non-parametric aspect of the proposed approach will allow greater robustness and sensitivity. This new approach will be beneficial for scientists, epidemiologists and econometricians by facilitating the detection of previously unknown relationships between variables.To ensure that the method can be used by the widest audience of researchers, its implementation will be distributed in an openly available software package in the R statistical programming platform. A website will be dedicated to the method and its practical applications, including simple examples illustrating the use of the R package.

条件独立性测试是现代因果发现的核心，它本身在整个科学和机器学习中都是至关重要的。特别是，因果关系的发现在公共卫生和流行病学、生命科学和地球科学、社会学研究和计量经济学中是基本的。基于随机试验的这种因果发现的传统方法是不可能的，在一些最重要的应用中甚至是不道德的。例如，调查吸烟或接触特定化学物质等环境因素对患者健康的影响，不能通过将患者随机分配到可能暴露于潜在非常有害因素的一组来进行研究。在这种情况下，确定一大组变量之间因果联系的唯一方法是利用日益丰富的观察性研究。从观测数据进行因果推断的大多数算法方法依赖于变量之间独立性和条件独立性的统计检验。在因果关系发现中，现有的最广泛使用的独立性和条件独立性检验，如皮尔逊相关性和偏相关性，只能检验线性相关性。虽然如果关系确实是线性的，这些方法是非常有效的，但它们对最具挑战性的应用程序中存在的复杂关系类型视而不见，在这些应用程序中，依赖项实际上是高度非线性的。为了正确地检测这些关系，不仅需要超越线性假设，而且需要使用非参数方法，该方法不假设变量之间的相关性形式。该项目致力于开发一种非参数统计方法来检验贝叶斯框架中的条件独立性。贝叶斯设置将提供严格的统计基础，而拟议方法的非参数方面将允许更强的稳健性和敏感性。这一新方法将有利于科学家、流行病学家和计量经济学家检测变量之间以前未知的关系。为了确保该方法能够被最广泛的研究人员使用，其实施将在R统计编程平台上以开放的软件包分发。一个网站将专门介绍该方法及其实际应用，包括说明R包使用的简单例子。