Causal inference framework for generalizable safety effect estimates

Jonathan S. Wood, Eric T. Donnell

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

This study integrates a causal inference framework to the Empirical Bayes (EB) before-after method to develop generalizable safety effect estimates (i.e., crash modification factor (CMF)). The method considers approaches to estimate the average treatment effect for the treated (ATT), average treatment effect for the untreated (ATU), and average treatment effect (ATE). The current EB method is shown to estimate ATT while ATE is what is typically desired in traffic safety research. Modifications to the current EB method to estimate ATU and ATE are provided. The method is then applied to a dataset with a “no-treatment” scenario where the treatments were: 1) randomly selected and 2) selected based on crash history. Given the “no-treatment” outcome, it is known that the CMFs should have a value of 1 in order to be considered accurate. The standard negative binomial and mixed effects negative binomial regression models were applied in the analysis. It was found that, of the two regression methods, the ATE CMFs developed using the standard negative binomial were the most accurate. Finally, potential sources of bias in the EB method are discussed.

Original languageEnglish (US)
Pages (from-to)74-87
Number of pages14
JournalAccident Analysis and Prevention
Volume104
DOIs
StatePublished - Jul 1 2017

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

Fingerprint Dive into the research topics of 'Causal inference framework for generalizable safety effect estimates'. Together they form a unique fingerprint.

Cite this