  This article presents one of the pioneering studies on causal modeling in
travel mode choice decision-making using causal discovery algorithms. These
models are a major advancement from conventional correlation-based techniques.
We propose a novel methodology that combines causal discovery with structural
equation modeling (SEM). This modeling approach overcomes some of the
limitations of SEM by combining the strengths of both causal discovery and SEM.
Causal discovery algorithms determine causal graphs from observational data and
domain knowledge, and SEMs estimate direct causal effects and test the
performance of causal discovery algorithms. In this study, we test four causal
discovery algorithms: Peter-Clark (PC), Fast Causal Inference (FCI), Fast
Greedy Equivalence Search (FGES), and Direct Linear Non-Gaussian Acyclic Models
(DirectLiNGAM). The results show that DirectLiNGAM based SEM model best
captures causality in mode choice behavior. It passes several goodness-of-fit
tests, including Root Mean Square Error of Approximation (RMSEA) and
Goodness-of-Fit Index (GFI), and it achieves the lowest Bayesian Information
Criterion (BIC) value. The analyses are conducted on data collected from the
2017 National Household Travel Survey in the New York Metropolitan area.
