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Causal inference in epidemiological studies with strong confounding
Author(s) -
Moore Kelly L.,
Neugebauer Romain,
Laan Mark J.,
Tager Ira B.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4469
Subject(s) - causal inference , identifiability , estimator , marginal structural model , confounding , econometrics , causal model , inverse probability , contrast (vision) , computer science , inference , psychological intervention , causality (physics) , set (abstract data type) , mathematics , statistics , bayesian probability , psychology , artificial intelligence , machine learning , posterior probability , programming language , physics , quantum mechanics , psychiatry
One of the identifiability assumptions of causal effects defined by marginal structural model (MSM) parameters is the experimental treatment assignment (ETA) assumption. Practical violations of this assumption frequently occur in data analysis when certain exposures are rarely observed within some strata of the population. The inverse probability of treatment weighted (IPTW) estimator is particularly sensitive to violations of this assumption; however, we demonstrate that this is a problem for all estimators of causal effects. This is due to the fact that the ETA assumption is about information (or lack thereof) in the data. A new class of causal models, causal models for realistic individualized exposure rules (CMRIER), is based on dynamic interventions. CMRIER generalize MSM, and their parameters remain fully identifiable from the observed data, even when the ETA assumption is violated, if the dynamic interventions are set to be realistic. Examples of such realistic interventions are provided. We argue that causal effects defined by CMRIER may be more appropriate in many situations, particularly those with policy considerations. Through simulation studies, we examine the performance of the IPTW estimator of the CMRIER parameters in contrast to that of the MSM parameters. We also apply the methodology to a real data analysis in air pollution epidemiology to illustrate the interpretation of the causal effects defined by CMRIER. Copyright © 2012 John Wiley & Sons, Ltd.