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- aggregation classification "A1".
- aggregation creator B506593.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 1078625.bibtex.
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- aggregation isPartOf urn:issn:0962-2802.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Mathematics and Statistics".
- aggregation title "On model selection and model misspecification in causal inference".
- aggregation abstract "Standard variable selection procedures, primarily developed for the construction of outcome prediction models, are routinely applied when assessing exposure effects in observational studies. We argue that this tradition is sub-optimal and prone to yield bias in exposure effect estimators as well as their corresponding uncertainty estimators. We weigh the pros and cons of confounder-selection procedures and propose a procedure directly targeting the quality of the exposure effect estimator. We further demonstrate that certain strategies for inferring causal effects have the desirable features (a) of producing (approximately) valid confidence intervals, even when the confounder-selection process is ignored, and (b) of being robust against certain forms of misspecification of the association of confounders with both exposure and outcome.".
- aggregation authorList BK850216.
- aggregation endPage "30".
- aggregation issue "1".
- aggregation startPage "7".
- aggregation volume "21".
- aggregation aggregates 1078627.
- aggregation aggregates 2097526.
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- aggregation similarTo 0962280210387717.
- aggregation similarTo LU-1078625.