title: Accident reduction factors and causal inference in traffic safety studies: a review.
abstract: Accident reduction factors are used to predict the change in accident occurrence which a countermeasure can be expected to cause. Since ethical and legal obstacles preclude the use of randomized experiments when evaluating traffic safety improvements, empirical support for the causal effectiveness of accident countermeasures comes entirely from observational studies. Drawing on developments in causal inference initiated by Donald Rubin, it is argued here that the mechanism by which sites are selected for application of a countermeasure should be included as part of a study's data model, and that when important features of the selection mechanism are neglected, existing methods for estimating accident reduction factors become inconsistent. A promising, but neglected, way out of these difficulties lies in developing rational countermeasure selection methods which also support valid causal inference of countermeasure effects.
authors.affiliation: Department of Civil Engineering, University of Minnesota, Minneapolis 55455, USA. drtrips@tc.umn.edu
authors.forename: G A
authors.initials: GA
authors.lastname: Davis
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D015984:Causality; D006801:Humans; D015233:Models, Statistical; D015397:Program Evaluation / Q000379:methods*; D017751:Safety Management / Q000706:statistics & numerical data*