  This paper quantifies the effect of speed cameras on road traffic collisions
using an approximate Bayesian doubly-robust (DR) causal inference estimation
method. Previous empirical work on this topic, which shows a diverse range of
estimated effects, is based largely on outcome regression (OR) models using the
Empirical Bayes approach or on simple before and after comparisons. Issues of
causality and confounding have received little formal attention. A causal DR
approach combines propensity score (PS) and OR models to give an average
treatment effect (ATE) estimator that is consistent and asymptotically normal
under correct specification of either of the two component models. We develop
this approach within a novel approximate Bayesian framework to derive posterior
predictive distributions for the ATE of speed cameras on road traffic
collisions. Our results for England indicate significant reductions in the
number of collisions at speed cameras sites (mean ATE = -15%). Our proposed
method offers a promising approach for evaluation of transport safety
interventions.
