FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765237624> ?p ?o ?g. }

• W2765237624 endingPage "78" @default.
• W2765237624 startingPage "53" @default.
• W2765237624 abstract "The problem of learning causal influences from passive data has attracted a good deal of attention in the past 30 years, and techniques have been developed and tested. These techniques assume the composition property, which entails that they cannot in general learn interactive causes with little marginal effects. However, such interactions are fairly commonplace. One notable example is genetic epistasis, which is the interaction of two or more genetic loci to affect phenotype. Often the genes exhibit little marginal effects. Another important example is the interaction of a treatment with patient features to affect outcomes. Even though efforts have recently been made towards developing new algorithms that discover such interactions from data, to our knowledge no definition of a discrete causal interaction has been forwarded. Using information theory, we develop a fuzzy definition of a discrete causal action, called Interaction Strength (IS). The IS is bounded above by 1 and equals 1 if the causes in the interaction exhibit no marginal effects. Using the IS and BN scoring, we develop an exhaustive search algorithm, Exhaustive-IGain, which learns interactions from low-dimension datasets, and a heuristic search algorithm, called MBS-IGain, which learns interactions from high-dimensional datasets. Using simulated high-dimensional datasets, based on models of genetic epistasis, we compare MBS-IGain to 7 algorithms that learn genetic epistasis from high-dimensional datasets, and show that MBS-IGain’s discovery performance is notably better than the other methods. We apply MBS-IGain to a real LOAD dataset, and obtain results substantiating previous research and new results. Using low-dimensional simulated datasets, we show Exhaustive-IGain can learn 4-cause interactions with no marginal effects. We apply Exhaustive-Gain to a real clinical breast cancer datasets, and learn interactions that agree with the judgements of a breast cancer oncologist. Our algorithms are only directly applicable to problems where we have a specified target and its candidate causes. However, our algorithms could be used for general causal learning by being a front end to a standard causal learning algorithm." @default.
• W2765237624 created "2017-11-10" @default.
• W2765237624 creator A5039264502 @default.
• W2765237624 creator A5066888888 @default.
• W2765237624 date "2017-10-20" @default.
• W2765237624 modified "2023-09-26" @default.
• W2765237624 title "Defining and Discovering Interactive Causes" @default.
• W2765237624 cites W1530964327 @default.
• W2765237624 cites W1532551225 @default.
• W2765237624 cites W1755360231 @default.
• W2765237624 cites W1980175560 @default.
• W2765237624 cites W1980452149 @default.
• W2765237624 cites W1980991473 @default.
• W2765237624 cites W1991993575 @default.
• W2765237624 cites W1995875735 @default.
• W2765237624 cites W2005825831 @default.
• W2765237624 cites W2005895632 @default.
• W2765237624 cites W2008352401 @default.
• W2765237624 cites W2013473112 @default.
• W2765237624 cites W2026475293 @default.
• W2765237624 cites W2041170649 @default.
• W2765237624 cites W2059836225 @default.
• W2765237624 cites W2070082005 @default.
• W2765237624 cites W2076220518 @default.
• W2765237624 cites W2090165362 @default.
• W2765237624 cites W2093805274 @default.
• W2765237624 cites W2109353557 @default.
• W2765237624 cites W2117538811 @default.
• W2765237624 cites W2126964466 @default.
• W2765237624 cites W2129961269 @default.
• W2765237624 cites W2131878646 @default.
• W2765237624 cites W2148916924 @default.
• W2765237624 cites W2152905639 @default.
• W2765237624 cites W2155781289 @default.
• W2765237624 cites W2157308673 @default.
• W2765237624 cites W2157492800 @default.
• W2765237624 cites W2161630867 @default.
• W2765237624 cites W2165654879 @default.
• W2765237624 cites W2169476035 @default.
• W2765237624 cites W2179629101 @default.
• W2765237624 cites W2184613487 @default.
• W2765237624 cites W2303043072 @default.
• W2765237624 cites W2405013394 @default.
• W2765237624 cites W2548666735 @default.
• W2765237624 cites W3214092816 @default.
• W2765237624 cites W4211007335 @default.
• W2765237624 doi "https://doi.org/10.1007/978-3-319-67513-8_4" @default.
• W2765237624 hasPublicationYear "2017" @default.
• W2765237624 type Work @default.
• W2765237624 sameAs 2765237624 @default.
• W2765237624 citedByCount "0" @default.
• W2765237624 crossrefType "book-chapter" @default.
• W2765237624 hasAuthorship W2765237624A5039264502 @default.
• W2765237624 hasAuthorship W2765237624A5066888888 @default.
• W2765237624 hasConcept C104317684 @default.
• W2765237624 hasConcept C111472728 @default.
• W2765237624 hasConcept C119857082 @default.
• W2765237624 hasConcept C124101348 @default.
• W2765237624 hasConcept C138885662 @default.
• W2765237624 hasConcept C154945302 @default.
• W2765237624 hasConcept C173801870 @default.
• W2765237624 hasConcept C185592680 @default.
• W2765237624 hasConcept C189950617 @default.
• W2765237624 hasConcept C41008148 @default.
• W2765237624 hasConcept C55493867 @default.
• W2765237624 hasConcept C61727976 @default.
• W2765237624 hasConceptScore W2765237624C104317684 @default.
• W2765237624 hasConceptScore W2765237624C111472728 @default.
• W2765237624 hasConceptScore W2765237624C119857082 @default.
• W2765237624 hasConceptScore W2765237624C124101348 @default.
• W2765237624 hasConceptScore W2765237624C138885662 @default.
• W2765237624 hasConceptScore W2765237624C154945302 @default.
• W2765237624 hasConceptScore W2765237624C173801870 @default.
• W2765237624 hasConceptScore W2765237624C185592680 @default.
• W2765237624 hasConceptScore W2765237624C189950617 @default.
• W2765237624 hasConceptScore W2765237624C41008148 @default.
• W2765237624 hasConceptScore W2765237624C55493867 @default.
• W2765237624 hasConceptScore W2765237624C61727976 @default.
• W2765237624 hasLocation W27652376241 @default.
• W2765237624 hasOpenAccess W2765237624 @default.
• W2765237624 hasPrimaryLocation W27652376241 @default.
• W2765237624 hasRelatedWork W1768435 @default.
• W2765237624 hasRelatedWork W1933229307 @default.
• W2765237624 hasRelatedWork W2391209197 @default.
• W2765237624 hasRelatedWork W2961085424 @default.
• W2765237624 hasRelatedWork W3046775127 @default.
• W2765237624 hasRelatedWork W3135523726 @default.
• W2765237624 hasRelatedWork W3149424243 @default.
• W2765237624 hasRelatedWork W4205958290 @default.
• W2765237624 hasRelatedWork W4286629047 @default.
• W2765237624 hasRelatedWork W4224009465 @default.
• W2765237624 isParatext "false" @default.
• W2765237624 isRetracted "false" @default.
• W2765237624 magId "2765237624" @default.
• W2765237624 workType "book-chapter" @default.