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dc.contributor.authorBahamyirou, Asma
dc.contributor.authorBlais, Lucie
dc.contributor.authorForget, Amélie
dc.contributor.authorSchnitzer, Mireille
dc.date.accessioned2020-03-06T13:25:14Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2020-03-06T13:25:14Z
dc.date.issued2018-05-02
dc.identifier.urihttp://hdl.handle.net/1866/23111
dc.publisherSAGEfr
dc.subjectCausal inferencefr
dc.subjectPositivityfr
dc.subjectDoubly robustfr
dc.subjectIPTWfr
dc.subjectSuper learnerfr
dc.titleUnderstanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimatorsfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté de pharmaciefr
dc.contributor.affiliationUniversité de Montréal. École de santé publique. Département de médecine sociale et préventivefr
dc.identifier.doi10.1177/0962280218772065
dcterms.abstractData-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produce a separation of the two exposure groups in terms of propensity score densities which can lead to biased estimates of the treatment effect. To motivate the problem, we evaluated the Targeted Minimum Loss-based Estimation procedure using a simulation scenario to estimate the average treatment effect. We highlight the divergence in estimates obtained when using parametric and data-adaptive methods to estimate the propensity score. We then adapted an existing diagnostic tool based on a bootstrap resampling of the subjects and simulation of the outcome data in order to show that the estimation using data-adaptive methods for the propensity score in this study may lead to large bias and poor coverage. The adapted bootstrap procedure is able to identify this instability and can be used as a diagnostic tool.fr
dcterms.isPartOfurn:ISSN:0962-2802fr
dcterms.isPartOfurn:ISSN:1477-0334fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposanthttps://doi.org/10.1177%2F0962280218772065fr
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleStatistical methods in medical research
oaire.citationVolume28
oaire.citationIssue6
oaire.citationStartPage1637
oaire.citationEndPage1650


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