Evaluating uses of data mining techniques in propensity score estimation: a simulation study

By S. Setoguchi, Sebastian Schneeweiss, M. Alan Brookhart, Robert J. Glynn, E. Cook. Pharmacoepidemiology and Drug Safety, 2008

301 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f75980cbd2a4a836d574bdb52297412afa5c9490

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Citations in the corpus

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, background): “…McCaffrey, Ridgeway, & Morral, 2004), recursive partitioning or tree-based methods (Lee et al., 2010; Setoguchi, Schneeweiss, Brookhart, Glynn, & Cook, 2008), random forests (Lee et al., 2010), and neural networks (Setoguchi et al., 2008) for estimating the propensity score have been examined.”
The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score. (2014, methodology): “(18) originally constructed these scenarios to reflect the complexities of treatment assignment that are likely to occur in practice.”
Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative (2011, background): “These last choices represent just two of a number different choices for flexible estimation that originate in the data mining literature (for some other choices see Setoguchi, Schneeweiss, Brookhart, Glynn, & Cook, 2008 ).”
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010, methodology): “Setoguchi also used classification trees to estimate propensity scores, generally finding that such propensity scores yielded effect estimates that were less biased than those derived using main effects logistic regression [9].”
The performance of different propensity score methods for estimating marginal hazard ratios (2007, result): “Furthermore, we would note that several prior studies examining the performance of propensity score methods for estimating treatment effects have employed Monte Carlo simulations [6–9, 31, 32].”
An introduction to propensity-score methods for reducing confounding in observational studies (2011, unknown): “, 2010), and neural networks (Setoguchi et al., 2008) for estimating the propensity score have been examined.”
Data mining techniques and applications - A decade review from 2000 to 2011 (2012, unknown): “Logistic regression Setoguchi et al. (2008)”
Doubly robust estimation of causal effects. (2011, methodology): “(Alternatively one could use, for example, a probit model or a machine learning approach (9, 10)).”
Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies (2006, methodology): “The first simulation study was developed by other researchers to demonstrate the effectiveness of machine learning algorithms for semiparametric estimation of the propensity score (Lee, Lessler, & Stuart, 2010; Setoguchi et al., 2008). We use this study to benchmark GenMatch against two alternative ”
Improving propensity score weighting using machine learning (2010, methodology): “We followed the simulation structure described by Setoguchi and colleagues with slight modiﬁcations [16].”