ITEM METADATA RECORD
Title: On model selection and model misspecification in causal inference
Authors: Vansteelandt, Stijn ×
Bekaert, Maarten
Claeskens, Gerda #
Issue Date: 2012
Publisher: Edward Arnold
Series Title: Statistical Methods in Medical Research vol:21 issue:1 pages:7-30
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.
ISSN: 0962-2802
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
onmodelselection.pdf Published 421KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members

 




All items in Lirias are protected by copyright, with all rights reserved.

© Web of science