From Observational Studies to Causal Rule Mining
文献类型：期刊论文
| 作者 | Li, Jiuyong1; Thuc Duy Le1; Liu, Lin1; Liu, Jixue1; Jin, Zhou2 ; Sun, Bingyu3; Ma, Saisai1
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| 刊名 | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
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| 出版日期 | 2016 |
| 卷号 | 7期号:2页码:1-27 |
| 关键词 | Algorithms Causal Discovery Association Rule Cohort Study Odds Ratio |
| DOI | 10.1145/2746410 |
| 文献子类 | Article |
| 英文摘要 | Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real-world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables-a feature that other causal discovery methods do not possess. |
| WOS关键词 | ASSOCIATION RULES ; CONTROLLED-TRIALS ; INFERENCE ; DISCOVERY |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000374446800002 |
| 资助机构 | Australian Research Council(DP130104090 ; Australian Research Council(DP130104090 ; Australian Research Council(DP130104090 ; Australian Research Council(DP130104090 ; DP140103617) ; DP140103617) ; DP140103617) ; DP140103617) ; Australian Research Council(DP130104090 ; Australian Research Council(DP130104090 ; Australian Research Council(DP130104090 ; Australian Research Council(DP130104090 ; DP140103617) ; DP140103617) ; DP140103617) ; DP140103617) |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/30891]  |
| 专题 | 合肥物质科学研究院_中科院合肥智能机械研究所 |
| 作者单位 | 1.Univ S Australia, Sch Informat Technol & Math Sci, Mawson Lakes, SA 5095, Australia 2.Univ Sci & Technol, Dept Automat, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Jiuyong,Thuc Duy Le,Liu, Lin,et al. From Observational Studies to Causal Rule Mining[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2016,7(2):1-27. |
| APA | Li, Jiuyong.,Thuc Duy Le.,Liu, Lin.,Liu, Jixue.,Jin, Zhou.,...&Ma, Saisai.(2016).From Observational Studies to Causal Rule Mining.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,7(2),1-27. |
| MLA | Li, Jiuyong,et al."From Observational Studies to Causal Rule Mining".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 7.2(2016):1-27. |
入库方式： OAI收割
来源：合肥物质科学研究院
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