  Causal analysis and classification of injury severity applying non-parametric
methods for traffic crashes has received limited attention. This study presents
a methodological framework for causal inference, using Granger causality
analysis, and injury severity classification of traffic crashes, occurring on
interstates, with different machine learning techniques including decision
trees (DT), random forest (RF), extreme gradient boosting (XGBoost), and deep
neural network (DNN). The data used in this study were obtained for traffic
crashes on all interstates across the state of Texas from a period of six years
between 2014 and 2019. The output of the proposed severity classification
approach includes three classes for fatal and severe injury (KA) crashes,
non-severe and possible injury (BC) crashes, and property damage only (PDO)
crashes. While Granger Causality helped identify the most influential factors
affecting crash severity, the learning-based models predicted the severity
classes with varying performance. The results of Granger causality analysis
identified the speed limit, surface and weather conditions, traffic volume,
presence of workzones, workers in workzones, and high occupancy vehicle (HOV)
lanes, among others, as the most important factors affecting crash severity.
The prediction performance of the classifiers yielded varying results across
the different classes. Specifically, while decision tree and random forest
classifiers provided the greatest performance for PDO and BC severities,
respectively, for the KA class, the rarest class in the data, deep neural net
classifier performed superior than all other algorithms, most likely due to its
capability of approximating nonlinear models. This study contributes to the
limited body of knowledge pertaining to causal analysis and classification
prediction of traffic crash injury severity using non-parametric approaches.
