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This paper quantifies the effect of speed cameras on road traffic collisionsusing an approximate Bayesian doubly-robust (DR) causal inference estimationmethod. Previous empirical work on this topic, which shows a diverse range ofestimated effects, is based largely on outcome regression (OR) models using theEmpirical Bayes approach or on simple before and after comparisons. Issues ofcausality and confounding have received little formal attention. A causal DRapproach combines propensity score (PS) and OR models to give an averagetreatment effect (ATE) estimator that is consistent and asymptotically normalunder correct specification of either of the two component models. We developthis approach within a novel approximate Bayesian framework to derive posteriorpredictive distributions for the ATE of speed cameras on road trafficcollisions. Our results for England indicate significant reductions in thenumber of collisions at speed cameras sites (mean ATE = -15%). Our proposedmethod offers a promising approach for evaluation of transport safetyinterventions.

Quantifying the causal effect of speed cameras on road traffic accidents via an approximate Bayesian doubly robust estimator