The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression
Open accessAbstract
Summary 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 (CART), and meta-classifiers (in particular, boosting). Conclusion While 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.
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Citations
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917
A tutorial on propensity score estimation for multiple treatments using generalized boosted models
McCaffrey, D. F., Griffin, B. A., Almirall, D., Slaughter, M. E., Ramchand, R., & Burgette, L. F. (2013) · Statistics in Medicine · doi:10.1002/sim.5753 · Cited by 1,450
Doubly robust estimation of causal effects
Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq439 · Cited by 1,158
Self-citationBalance diagnostics after propensity score matching
written on behalf of AME Big-Data Clinical Trial Collaborative Group, Zhang, Z., Kim, H. J., Lonjon, G., & Zhu, Y. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2018.12.10 · Cited by 1,031
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., & Tzovaras, D. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2020.100341 · Cited by 341
Metadata
- Authors
- Westreich, D., Lessler, J., & Funk, M. J.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2010-08
- Volume
- 63
- Issue
- 8
- Pages
- 826-833
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2002646960
- Cited by
- 502
- References
- 45
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- green
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- Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2009.11.020
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