Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.
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.


METHODS
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).


CONCLUSIONS
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.
Westreich
Daniel
D
Department of Epidemiology, University of North Carolina at Chapel Hill, NC 27599-7435, USA. djw@unc.edu
Lessler
Justin
J
Funk
Michele Jonsson
MJ
D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D057216:Propensity Score