Causal Inference from Partial Statistical Information

统计理论的大部分内容都是关于从实验、调查或研究中获得的单一数据集的分析。然而，在许多迫切需要新的统计方法的领域，问题并不适合这种范式。相反，数据是在不同的实验条件下从多个设置中收集的，这些设置可能测量不同的变量或从不同的人群中取样。这导致了不清楚如何将统计信息以一种方式结合起来，提供一个与所有研究一致的一致解决方案，并适当地量化估计过程中的不确定性。回答因果问题的金标准方法是随机对照试验（RCT），但RCT非常昂贵，而且通常没有积极的结果。与此同时，生物学和医学正处于大数据革命的最前沿，因为越来越多的数据正在以越来越高的分辨率进行测量; 10万个基因组计划和英国生物银行都包含了数十万人的数万个基因和表型测量结果。电子医疗记录将产生数千万人的TB级医疗信息。由于实际、经济或伦理方面的原因，这些数据集中测量的许多量无法通过实验控制。该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，对多个大型复杂数据集下的因果关系机制了解多少：通过观察尽可能多地了解世界。

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.