  This study compares the performance of a causal and a predictive model in
modeling travel mode choice in three neighborhoods in Chicago. A causal
discovery algorithm and a causal inference technique were used to extract the
causal relationships in the mode choice decision making process and to estimate
the quantitative causal effects between the variables both directly from
observational data. The model results reveal that trip distance and vehicle
ownership are the direct causes of mode choice in the three neighborhoods.
Artificial neural network models were estimated to predict mode choice. Their
accuracy was over 70%, and the SHAP values obtained measure the importance of
each variable. We find that both the causal and predictive modeling approaches
are useful for the purpose they serve. We also note that the study of mode
choice behavior through causal modeling is mostly unexplored, yet it could
transform our understanding of the mode choice behavior. Further research is
needed to realize the full potential of these techniques in modeling mode
choice.
