Practical Approaches to Causal Relationship Exploration
This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for caus...
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2015
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oai:scholar.dlu.edu.vn:DLU123456789-586232023-11-11T06:13:22Z Practical Approaches to Causal Relationship Exploration Li, Jiuyong Liu, Lin Le, Thuc Data mining Causation Computers General This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery. 2015-09-30T02:06:44Z 2015-09-30T02:06:44Z 2015 Book 978-3-319-14433-7 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58623 en application/pdf Springer |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Data mining Causation Computers General |
| spellingShingle |
Data mining Causation Computers General Li, Jiuyong Liu, Lin Le, Thuc Practical Approaches to Causal Relationship Exploration |
| description |
This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery. |
| format |
Book |
| author |
Li, Jiuyong Liu, Lin Le, Thuc |
| author_facet |
Li, Jiuyong Liu, Lin Le, Thuc |
| author_sort |
Li, Jiuyong |
| title |
Practical Approaches to Causal Relationship Exploration |
| title_short |
Practical Approaches to Causal Relationship Exploration |
| title_full |
Practical Approaches to Causal Relationship Exploration |
| title_fullStr |
Practical Approaches to Causal Relationship Exploration |
| title_full_unstemmed |
Practical Approaches to Causal Relationship Exploration |
| title_sort |
practical approaches to causal relationship exploration |
| publisher |
Springer |
| publishDate |
2015 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58623 |
| _version_ |
1782535211773329408 |