In this section
@article{Li:2019:10.1016/j.aap.2019.05.015,
author = {Li, H and Graham, DJ and Ding, H and Ren, G},
doi = {10.1016/j.aap.2019.05.015},
journal = {Accident Analysis and Prevention},
pages = {148--155},
title = {Comparison of empirical Bayes and propensity score methods for road safety evaluation: a simulation study},
url = {http://dx.doi.org/10.1016/j.aap.2019.05.015},
volume = {129},
year = {2019}
}
TY - JOUR
AB - Statistical evaluation of road safety interventions can be undertaken using a variety of different approaches, typically requiring different assumptions to obtain causal identification. In this paper, we conduct a simulation study to compare the performance of empirical Bayes (EB) and propensity score (PS) based methods, which have featured prominently in the recent literature, in settings with and without violation of key assumptions. The estimators considered include EB, inverse probability weighting (IPW), and Doubly Robust (DR) estimation. We find that while the EB approach has good finite sample properties when model assumptions are met, the consistency of this estimator is substantially diminished when the reference and treated sites follow different functions. The IPW estimator performs well in large samples, but requires a correctly specified PS model with sufficient overlap in covariate distributions between treated and control units. The DR estimator allows for violation of assumptions in either the regression or PS model, but not both. We find that this added level of robustness affords overall better performance than attained via EB or IPW estimation.
AU - Li,H
AU - Graham,DJ
AU - Ding,H
AU - Ren,G
DO - 10.1016/j.aap.2019.05.015
EP - 155
PY - 2019///
SN - 0001-4575
SP - 148
TI - Comparison of empirical Bayes and propensity score methods for road safety evaluation: a simulation study
T2 - Accident Analysis and Prevention
UR - http://dx.doi.org/10.1016/j.aap.2019.05.015
UR - http://hdl.handle.net/10044/1/70431
VL - 129
ER -
Transport Strategy Centre
Department of Civil and Environmental Engineering
South Kensington Campus
Imperial College London
London SW7 2AZ - UK
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