权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Causal Inference from Partial Statistical Information

从部分统计信息进行因果推断

基本信息

批准号：
EP/N020294/1
负责人：
Robin Evans
金额：
$ 12.62万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN020294%2F1
关键词：
Causal Inference Partial Statistical Information

项目摘要

Much of statistical theory is concerned with the analysis of a singledata set, obtained from an experiment, survey, or study. However inmany fields which most urgently demand new statistical methodology,problems do not fit this paradigm. Instead data are gathered frommultiple settings under different experimental conditions, which maymeasure different variables or be sampling from differentpopulations. This leads to situations in which it is unclear how tocombine statistical information in a way that provides a coherentsolution agreeing with all studies, and which properly quantifies theuncertainty in the estimation process.The gold-standard method for answering causal questions is therandomised controlled trial (RCT), but RCTs are extremely expensiveand usually end without a positive result. Meanwhile biology andmedicine are at the forefront of the big data revolution, as more andmore is being measured at greater and greater resolutions; the 100,000Genomes Project and UK Biobank each contain tens of thousands ofgenetic and phenotypic measurements on hundreds of thousands ofpeople. Electronic healthcare records will generate Terabytes ofmedical information about tens of millions of people. Many of thequantities measured in these data sets cannot be experimentallycontrolled for practical, financial or ethical reasons.This project aims to uncover how much we can learn about the causalmechanisms underlying multiple large and complex data sets withoutperforming experiments, or with limited experimental data: to learn as much as possible about the world just by looking.

许多统计学理论都涉及对从实验、调查或研究中获得的单一数据集的分析。然而，在许多最迫切需要新的统计方法的领域，问题并不符合这一范式。相反，数据是从不同实验条件下的多个环境中收集的，这些数据可以测量不同的变量，也可以从不同的人群中抽样。这导致不清楚如何结合统计信息，以提供与所有研究一致的解决方案，并适当量化估计过程中的不确定性。回答因果问题的黄金标准方法是随机对照试验(RCT)，但随机对照试验非常昂贵，通常结束时没有积极的结果。与此同时，随着越来越多的人以越来越高的分辨率进行测量，生物学和医学走在了大数据革命的前沿；10万基因组计划和英国生物库分别包含数十万人的基因和表型测量数据。电子医疗记录将产生数千万人的万亿字节医疗信息。由于实际、财务或伦理方面的原因，在这些数据集中测量的许多量都无法通过实验进行控制。这个项目旨在揭示我们可以在不进行实验或使用有限的实验数据的情况下，对多个大型而复杂的数据集背后的因果机制了解多少：仅通过观察就能尽可能多地了解世界。