Causal discovery algorithms: A practical guide.
Abstract: Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observation...
| Publicado en: | Philosophy Compass Vol. 13; no. 1; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=127272732&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 127272732 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 17479991 1WQN jtl: Philosophy Compass issn: 17479991 maglogo: Y pubinfo: dt: Jan2018 vid: 13 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 127272732 10.1111/phc3.12470 ppf: 1 ppct: 1 formats: tig: atl: Causal discovery algorithms: A practical guide. aug: au: Malinsky, Daniel Danks, David affil: Department of Philosophy, Carnegie Mellon University Departments of Philosophy and Psychology, Carnegie Mellon University su: Algorithms Philosophy Computer software Algorithmic randomness Data analysis sug: subj: Algorithms Philosophy Computer software Algorithmic randomness Data analysis ab: Abstract: Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a “user's guide” to these methods, though not in the sense of specifying exact button presses in a software package. Instead, we explain the larger “pipeline” within which these methods are used and discuss key steps in moving from initial research idea to validated causal structure. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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