Efficient causal discovery from observational data

发现因果关系是许多应用程序的最终目标。随机对照试验是发现因果关系的金标准。然而，由于成本和/或伦理问题，在许多情况下进行此类试验是不可能的。相比之下，所有领域都积累了大量数据。人们希望直接和自动地从数据中推断因果关系。该项目旨在开发快速和可扩展的数据挖掘方法，用于从大型和/或高维数据集中识别因果关系。所开发的方法将主要在真实的世界生物应用中进行评估。研究成果将在许多领域的因果推理和决策有用。

Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop fast and scalable data mining methods for identifying causal relationships from large and/or high dimensional data sets. The developed methods will mainly be evaluated in real world biological applications. The research outcomes will be useful in many areas for causal reasoning and decision making.