Causal Inference from Noise.
Correlation is not causation is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell wh...
| Publicado en: | Nous (0029-4624) Vol. 55; no. 1; pp. 152 - 171 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Mar2021
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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=148631618&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 148631618 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00294624 D97 jtl: Nous (0029-4624) issn: 00294624 maglogo: Y pubinfo: dt: Mar2021 vid: 55 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 148631618 10.1111/nous.12300 ppf: 152 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 543KB tig: atl: Causal Inference from Noise. aug: au: Climenhaga, Nevin DesAutels, Lane Ramsey, Grant affil: Australian Catholic University Missouri Western State University KU Leuven su: Causal inference Inferential statistics Noise Randomized controlled trials sug: subj: Causal inference Inferential statistics Noise Randomized controlled trials ab: Correlation is not causation is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Nous (0029-4624) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Nous (0029-4624) holder: Wiley-Blackwell dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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