Article

Improving Propensity Score Weighting Using Machine Learning

Department of Epidemiology and Biostatistics, Drexel University School of Public Health, Philadelphia, PA 19102, U.S.A.
Statistics in Medicine (Impact Factor: 1.83). 11/2009; 29(3):337-46. DOI: 10.1002/sim.3782
Source: PubMed

ABSTRACT

Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous outcome, and 10 covariates were simulated under seven scenarios differing by degree of non-linear and non-additive associations between covariates and the exposure. Propensity score weights were estimated using logistic regression (all main effects), CART, pruned CART, and the ensemble methods of bagged CART, random forests, and boosted CART. Performance metrics included covariate balance, standard error, per cent absolute bias, and 95 per cent confidence interval (CI) coverage. All methods displayed generally acceptable performance under conditions of either non-linearity or non-additivity alone. However, under conditions of both moderate non-additivity and moderate non-linearity, logistic regression had subpar performance, whereas ensemble methods provided substantially better bias reduction and more consistent 95 per cent CI coverage. The results suggest that ensemble methods, especially boosted CART, may be useful for propensity score weighting.

    • "Cependant cette approche semble peu réalisable dans la pratique notamment en raison des données manquantes. Concernant la méthode à utiliser pour la création du score, des approches alternatives à la régression logistique comme les arbres de régression, ou le machine learning peuvent être utilisés [9]. Si davantage de modalités thérapeutiques ( > 2) sont étudiées, les analyses discriminantes ou en cluster (calcul de distances) peuvent être envisagées [10]. "

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    • "how many iterations), and a shrinkage parameter (i.e., the " learning rate " or how much change to make for each new regression tree) (Karwa et al., 2011; Lee et al., 2010; McCaffrey et al., 2004; Westreich et al., 2010; Wyss et al., 2014 "
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    ABSTRACT: A sufficient understanding of the safety impact of lane widths in urban areas is necessary to produce geometric designs that optimize safety performance for all users. The overarching trend found in the research literature is that as lane widths narrow, crash frequency increases. However, this trend is inconsistent and is the result of multiple cross-sectional studies that have issues related to lack of control for potential confounding variables, unobserved heterogeneity or omitted variable bias, or endogeneity among independent variables, among others. Using ten years of mid-block crash data on urban arterials and collectors from four cities in Nebraska, crash modification factors (CMFs) were estimated for various lane widths and crash types. These CMFs were developed using the propensity scores-potential outcomes methodology. This method reduces many of the issues associated with cross-sectional regression models when estimating the safety effects of infrastructure-related design features. Generalized boosting, a non-parametric modeling technique, was used to estimate the propensity scores. Matching was performed using both Nearest Neighbor and Mahalanobis matching techniques. CMF estimation was done using mixed-effects negative binomial or Poisson regression with the matched data. Lane widths included in the analysis included 9ft, 10ft, 11ft, and 12ft. Some of the estimated CMFs were point estimates while others were functions of traffic volume (i.e., the CMF changed depending on the traffic volume). Roadways with 10ft travel lanes were found to experience the highest crash frequency relative to other lane widths. Meanwhile, roads with 9ft travel lanes were found to experience the lowest relative crash frequency. While this may be due to increased driver caution when traveling on narrow lanes, it is possible that unobserved factors influenced this result. CMFs for target crash types (sideswipe same-direction and sideswipe opposite-direction) were consistent with the values currently used in the Highway Safety Manual (HSM). Copyright © 2015 Elsevier Ltd. All rights reserved.
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    • "However , weighting approaches may yield biased and inefficient estimates when the propensity score model is misspecified (Kang and Schafer, 2007). This problem can be overcome using a boosted classification and regression trees approach (boosted CART; McCaffrey et al., 2004), which can produce very accurate estimated propensity scores (Lee et al., 2010). However, no propensity score techniques can account for the possible presence of unobserved confounders. "
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