Discovering Context Specific Causal Relationships
| Autor: | Saisai Ma, Lin Liu, Thuc Duy Le, Jiuyong Li |
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| Přispěvatelé: | Ma, Saisai, Li, Jiuyong, Liu, Lin, Le, Thuc Duy |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Focus (computing) decision trees Computer Science - Artificial Intelligence Computer science Decision tree Context (language use) 02 engineering and technology context specific causal rules Statistics - Applications Data science Theoretical Computer Science potential outcome model Artificial Intelligence (cs.AI) Artificial Intelligence 020204 information systems Causal inference Context specific 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Observational study Applications (stat.AP) Computer Vision and Pattern Recognition Tree based Real world data |
| DOI: | 10.48550/arxiv.1808.06316 |
| Popis: | With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships. Most existing methods, however, assume a known cause (e.g. a new drug) and focus on identifying from data the contexts of heterogeneous effects of the cause (e.g. patient groups with different responses to the new drug). There is no approach to efficiently detecting directly from observational data context specific causal relationships, i.e. discovering the causes and their contexts simultaneously. In this paper, by taking the advantages of highly efficient decision tree induction and the well established causal inference framework, we propose the Tree based Context Causal rule discovery (TCC) method, for efficient exploration of context specific causal relationships from data. Experiments with both synthetic and real world data sets show that TCC can effectively discover context specific causal rules from the data. Comment: This paper has been accepted by Intelligent Data Analysis |
| Databáze: | OpenAIRE |
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