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008 150302s2015 gw | s |||| 0|eng d
020 _a9783319144337
_z978-3-319-14433-7
024 7 _a10.1007/978-3-319-14433-7
_2doi
040 _aTR-AnTOB
_beng
_cTR-AnTOB
_erda
050 4 _aQ334-342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema006.3
_223
100 1 _aLi, Jiuyong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aPractical Approaches to Causal Relationship Exploration /
_cby Jiuyong Li, Lin Liu, Thuc Duy Le.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
505 0 _aIntroduction -- Local causal discovery with a simple PC algorithm -- A local causal discovery algorithm for high dimensional data -- Causal rule discovery with partial association test -- Causal rule discovery with cohort studies -- Experimental comparison and discussions.
520 _aThis 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.
650 0 _aArtificial intelligence.
650 0 _aData mining.
650 1 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aData Mining and Knowledge Discovery.
_0http://scigraph.springernature.com/things/product-market-codes/I18030
700 1 _aLiu, Lin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aLe, Thuc Duy.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-3-319-14433-7
_3Springer eBooks
_zOnline access link to the resource
942 _2lcc
_cEBK
041 _aeng