  The research questions that motivate transportation safety studies are causal
in nature. Safety researchers typically use observational data to answer such
questions, but often without appropriate causal inference methodology. The
field of causal inference presents several modeling frameworks for probing
empirical data to assess causal relations. This paper focuses on exploring the
applicability of two such modeling frameworks---Causal Diagrams and Potential
Outcomes---for a specific transportation safety problem. The causal effects of
pavement marking retroreflectivity on safety of a road segment were estimated.
More specifically, the results based on three different implementations of
these frameworks on a real data set were compared: Inverse Propensity Score
Weighting with regression adjustment and Propensity Score Matching with
regression adjustment versus Causal Bayesian Network. The effect of increased
pavement marking retroreflectivity was generally found to reduce the
probability of target nighttime crashes. However, we found that the magnitude
of the causal effects estimated are sensitive to the method used and to the
assumptions being violated.
