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On model selection and model misspecification in causal inference

Stijn Vansteelandt UGent, Maarten Bekaert UGent and Gerda Claeskens (2012) STATISTICAL METHODS IN MEDICAL RESEARCH. 21(1). p.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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
double robustness, confounder-selection, influential weights, model selection, model uncertainty, propensity score, causal inference, MARGINAL STRUCTURAL MODELS, FOCUSED INFORMATION CRITERION, VARIABLE SELECTION, CONFOUNDER-SELECTION, PROPENSITY SCORE, INCOMPLETE DATA, EPIDEMIOLOGIC RESEARCH, SEMIPARAMETRIC MODELS, RANDOMIZED-TRIALS, BIAS
journal title
STATISTICAL METHODS IN MEDICAL RESEARCH
Stat. Methods Med. Res.
volume
21
issue
1
pages
7 - 30
Web of Science type
Article
Web of Science id
000299122600002
JCR category
STATISTICS & PROBABILITY
JCR impact factor
2.364 (2012)
JCR rank
7/117 (2012)
JCR quartile
1 (2012)
ISSN
0962-2802
DOI
10.1177/0962280210387717
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1078625
handle
http://hdl.handle.net/1854/LU-1078625
date created
2010-11-23 16:19:23
date last changed
2012-05-02 16:30:19
@article{1078625,
  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.},
  author       = {Vansteelandt, Stijn and Bekaert, Maarten and Claeskens, Gerda},
  issn         = {0962-2802},
  journal      = {STATISTICAL METHODS IN MEDICAL RESEARCH},
  keyword      = {double robustness,confounder-selection,influential weights,model selection,model uncertainty,propensity score,causal inference,MARGINAL STRUCTURAL MODELS,FOCUSED INFORMATION CRITERION,VARIABLE SELECTION,CONFOUNDER-SELECTION,PROPENSITY SCORE,INCOMPLETE DATA,EPIDEMIOLOGIC RESEARCH,SEMIPARAMETRIC MODELS,RANDOMIZED-TRIALS,BIAS},
  language     = {eng},
  number       = {1},
  pages        = {7--30},
  title        = {On model selection and model misspecification in causal inference},
  url          = {http://dx.doi.org/10.1177/0962280210387717},
  volume       = {21},
  year         = {2012},
}

Chicago
Vansteelandt, Stijn, Maarten Bekaert, and Gerda Claeskens. 2012. “On Model Selection and Model Misspecification in Causal Inference.” Statistical Methods in Medical Research 21 (1): 7–30.
APA
Vansteelandt, S., Bekaert, M., & Claeskens, G. (2012). On model selection and model misspecification in causal inference. STATISTICAL METHODS IN MEDICAL RESEARCH, 21(1), 7–30.
Vancouver
1.
Vansteelandt S, Bekaert M, Claeskens G. On model selection and model misspecification in causal inference. STATISTICAL METHODS IN MEDICAL RESEARCH. 2012;21(1):7–30.
MLA
Vansteelandt, Stijn, Maarten Bekaert, and Gerda Claeskens. “On Model Selection and Model Misspecification in Causal Inference.” STATISTICAL METHODS IN MEDICAL RESEARCH 21.1 (2012): 7–30. Print.