From Observational Studies to Causal Rule Mining
نویسندگان
چکیده
منابع مشابه
Using data mining techniques to characterize participation in observational studies.
Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose t...
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Business rules represent the knowledge that guides the operations of a business organization. They are implemented in software applications used by organizations, and the activity of extracting them from software is known as business rule mining. It has various purposes amongst which migration and generating documentation are the most common. However, apart from conventional software, organizat...
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Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We show that structure learning is successful when the causal systems in question are consistent with people’s expectations that causal relationships are deterministic and that each pattern of observations has a single underlying cause. Our data are well explained by a B...
متن کاملCausal Inference in Anesthesia and Perioperative Observational Studies
Purpose of Review Observational studies are of great importance to anesthesia and perioperative care research, as they reflect routine clinical practice. However, because observational data are nonexperimental, assigning causality to identified relationships has a significant risk of bias. After describing the pros and cons of observational studies, we provide an overview of the different metho...
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ژورنال
عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology
سال: 2016
ISSN: 2157-6904,2157-6912
DOI: 10.1145/2746410