Efficient causal discovery from observational data

从观测数据中有效发现因果关系

• 批准号: DP140103617
• 负责人: Prof Jiuyong Li
• 金额: $ 18.88万
• 依托单位: University of South Australia
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-06-19 至 2017-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140103617
• 关键词: Efficient; causal; discovery; observational; data

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.

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黄金标准。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，各个领域都积累了大量的数据。从数据中直接和自动地推断出因果关系是可取的。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现实世界的生物应用中进行评估。研究结果将在许多领域对因果推理和决策有用。