  Deep neural networks are increasingly utilized in mobility prediction tasks,
yet their intricate internal workings pose challenges for interpretability,
especially in comprehending how various aspects of mobility behavior affect
predictions. This study introduces a causal intervention framework to assess
the impact of mobility-related factors on neural networks designed for next
location prediction -- a task focusing on predicting the immediate next
location of an individual. To achieve this, we employ individual mobility
models to synthesize location visit sequences and control behavior dynamics by
intervening in their data generation process. We evaluate the interventional
location sequences using mobility metrics and input them into well-trained
networks to analyze performance variations. The results demonstrate the
effectiveness in producing location sequences with distinct mobility behaviors,
thereby facilitating the simulation of diverse yet realistic spatial and
temporal changes. These changes result in performance fluctuations in next
location prediction networks, revealing impacts of critical mobility behavior
factors, including sequential patterns in location transitions, proclivity for
exploring new locations, and preferences in location choices at population and
individual levels. The gained insights hold value for the real-world
application of mobility prediction networks, and the framework is expected to
promote the use of causal inference to enhance the interpretability and
robustness of neural networks in mobility applications.
