Bias mitigation in empirical peace and conflict studies: A short primer on posttreatment variables.
Posttreatment variables are covariates that are preceded by the main explanatory variable. Their inclusion in a statistical model does not 'control' for their influence on the relationship of interest, and it does not substitute for a mediation analysis. Likewise, a coefficient estimate of an approp...
| Publicado en: | Journal of Peace Research Vol. 61; no. 3; pp. 462 - 477 |
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| Formato: | Artículo |
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Oxford University Press / USA
May2024
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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=ssf&AN=177167199&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177167199 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223433 JPR jtl: Journal of Peace Research issn: 00223433 maglogo: Y pubinfo: dt: May2024 vid: 61 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 177167199 10.1177/00223433221145531 ppf: 462 ppct: 15 formats: tig: atl: Bias mitigation in empirical peace and conflict studies: A short primer on posttreatment variables. aug: au: Dworschak, Christoph affil: Department of Politics, University of York su: Common misconceptions Peace Political science Medical misconceptions Statistical models Computer software development sug: subj: Common misconceptions Peace Political science Custom Computer Programming Services Computer systems design and related services (except video game design and development) Medical misconceptions Statistical models Computer software development keyword: causal inference model specification peace and conflict political analysis posttreatment bias causal inference model specification peace and conflict political analysis posttreatment bias ab: Posttreatment variables are covariates that are preceded by the main explanatory variable. Their inclusion in a statistical model does not 'control' for their influence on the relationship of interest, and it does not substitute for a mediation analysis. Likewise, a coefficient estimate of an appropriate 'control variable' cannot be interpreted as a causal effect estimate. While these facts are well-established in various fields across the social sciences, their recognition in the field of peace and conflict studies is more limited. Originally collected data on recent publications from leading peace and conflict journals reveal that a large majority of evaluated articles condition on posttreatment variables, demonstrating how a review of these fallacies can help to substantially improve future research on peace and conflict. Drawing on a broad set of literature and using graphical approaches, I offer an intuitive explanation of the logic of posttreatment variables and clarify common misconceptions. Building on recent developments in methodology and software, and by deriving conditions for bounding using analytical bias expressions, I discuss avenues for dealing with posttreatment variables in observational studies. The article concludes with a discussion of implications for applied research. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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