Formulating causal questions and principled statistical answers

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Goetghebeur, ElsUniv Ghent, Dept Appl Math Comp Sci & Stat, Ghent, Belgium.;Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden. (författare)

Formulating causal questions and principled statistical answers

Artikel/kapitelEngelska2020

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2020-09-23
WILEY,2020
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:uu-439132
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-439132URI
https://doi.org/10.1002/sim.8741DOI
http://kipublications.ki.se/Default.aspx?queryparsed=id:144731511URI
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-418045URI

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Språk:engelska
Sammanfattning på:engelska

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Although review papers on causal inference methods are now available, there is a lack of introductory overviews onwhatthey can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on , where SAS and Stata code for analysis is also provided.

Ämnesord och genrebeteckningar

NATURVETENSKAP Matematik Sannolikhetsteori och statistik hsv//swe
NATURAL SCIENCES Mathematics Probability Theory and Statistics hsv//eng
causation
instrumental variable
inverse probability weighting
matching
potential outcomes
propensity score

Biuppslag (personer, institutioner, konferenser, titlar ...)

le Cessie, SaskiaLeiden Univ, Med Ctr, Dept Clin Epidemiol Biomed Data Sci, Leiden, Netherlands. (författare)
De Stavola, BiancaUCL, Great Ormond St Inst Child Hlth, London, England. (författare)
Moodie, Erica E. M.McGill Univ, Div Biostat, Montreal, PQ, Canada. (författare)
Waernbaum, Ingeborg,1972-Uppsala universitet,Statistiska institutionen(Swepub:uu)ingwa901 (författare)
Univ Ghent, Dept Appl Math Comp Sci & Stat, Ghent, Belgium.;Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.Leiden Univ, Med Ctr, Dept Clin Epidemiol Biomed Data Sci, Leiden, Netherlands. (creator_code:org_t)

Sammanhörande titlar

Ingår i:Statistics in Medicine: WILEY39:30, s. 4922-49480277-67151097-0258

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Av författaren/redakt...: Goetghebeur, Els; le Cessie, Saski ...; De Stavola, Bian ...; Moodie, Erica E. ...; Waernbaum, Ingeb ...

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NATURVETENSKAP: NATURVETENSKAP; och Matematik; och Sannolikhetsteor ...

Artiklar i publikationen: Statistics in Me ...

Av lärosätet: Uppsala universitet; Karolinska Institutet

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