Journal
MULTIVARIATE BEHAVIORAL RESEARCH
Volume 47, Issue 1, Pages 115-135Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/00273171.2012.640600
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Funding
- Institute for Clinical Evaluative Sciences (ICES)
- Ontario Ministry of Health and Long-Term Care (MOHLTC)
- Heart and Stroke Foundation of Ontario
- Canadian Institutes of Health Research (CIHR) [MOP 86508]
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Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one requires the potential outcome under each possible treatment. However, only the outcome under the actual treatment received is observed, whereas the potential outcomes under the other treatments are considered missing data. Some authors have proposed that parametric regression models be used to estimate potential outcomes. In this study, we examined the use of ensemble-based methods (bagged regression trees, random forests, and boosted regression trees) to directly estimate average treatment effects by imputing potential outcomes. We used an extensive series of Monte Carlo simulations to estimate bias, variance, and mean squared error of treatment effects estimated using different ensemble methods. For comparative purposes, we compared the performance of these methods with inverse probability of treatment weighting using the propensity score when logistic regression or ensemble methods were used to estimate the propensity score. Using boosted regression trees of depth 3 or 4 to impute potential outcomes tended to result in estimates with bias equivalent to that of the best performing methods. Using an empirical case study, we compared inferences on the effect of in-hospital smoking cessation counseling on subsequent mortality in patients hospitalized with an acute myocardial infarction.
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