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Citation

Westreich, Daniel; Lessler, Justin; & Funk, Michele Jonsson (2010). Propensity Score Estimation: Neural Networks, Support Vector Machines, Decision Trees (CART), and Meta-Classifiers as Alternatives to Logistic Regression. Journal of Clinical Epidemiology, 63(8), 826-833. PMCID: PMC2907172

Abstract

OBJECTIVE: Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy.
STUDY DESIGN AND SETTING: We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use.
RESULTS: We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting).
CONCLUSION: Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice.

URL

http://dx.doi.org/10.1016/j.jclinepi.2009.11.020

Reference Type

Journal Article

Year Published

2010

Journal Title

Journal of Clinical Epidemiology

Author(s)

Westreich, Daniel
Lessler, Justin
Funk, Michele Jonsson

PMCID

PMC2907172

ORCiD

Lessler - 0000-0002-9741-8109