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2021-12-11Zeitschriftenartikel DOI: 10.18452/26606
Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models
dc.contributor.authorGische, Christian
dc.contributor.authorVoelkle, Manuel C.
dc.date.accessioned2023-05-25T12:31:08Z
dc.date.available2023-05-25T12:31:08Z
dc.date.issued2021-12-11none
dc.date.updated2023-03-25T18:02:19Z
dc.identifier.issn0033-3123
dc.identifier.urihttp://edoc.hu-berlin.de/18452/27306
dc.description.abstractGraph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to define, identify, and estimate a broad class of causal quantities in linearly parametrized graph-basedmodels. The proposed method extends the literature, which mainly focuses on causal effects on the mean level and the variance of an outcome variable. For example, we show how to compute the probability that an outcome variable realizes within a target range of values given an intervention, a causal quantity we refer to as the probability of treatment success. We link graphbased causal quantities defined via the do-operator to parameters of the model implied distribution of the observed variables using so-called causal effect functions. Based on these causal effect functions, we propose estimators for causal quantities and show that these estimators are consistent and converge at a rate of N−1/2 under standard assumptions. Thus, causal quantities can be estimated based on sample sizes that are typically available in the social and behavioral sciences. In case of maximum likelihood estimation, the estimators are asymptotically efficient. We illustrate the proposed method with an example based on empirical data, placing special emphasis on the difference between the interventional and conditional distribution.eng
dc.description.sponsorshipHumboldt-Universität zu Berlin (1034)
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcausal inferenceeng
dc.subjectstructural equation modelingeng
dc.subjectgraph-based causal modelseng
dc.subjectacyclic directed mixed graphseng
dc.subject.ddc510 Mathematiknone
dc.subject.ddc150 Psychologienone
dc.titleBeyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Modelsnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/27306-1
dc.identifier.doihttp://dx.doi.org/10.18452/26606
dc.type.versionpublishedVersionnone
local.edoc.pages34none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn1860-0980
dcterms.bibliographicCitation.doi10.1007/s11336-021-09811-znone
dcterms.bibliographicCitation.journaltitlePsychometrikanone
dcterms.bibliographicCitation.volume87none
dcterms.bibliographicCitation.issue3none
dcterms.bibliographicCitation.originalpublishernameSpringer-Verl.none
dcterms.bibliographicCitation.originalpublisherplaceNew Yorknone
dcterms.bibliographicCitation.pagestart868none
dcterms.bibliographicCitation.pageend901none
bua.departmentLebenswissenschaftliche Fakultätnone

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