  This paper introduces a new causal structure learning method for
nonstationary time series data, a common data type found in fields such as
finance, economics, healthcare, and environmental science. Our work builds upon
the constraint-based causal discovery from nonstationary data algorithm
(CD-NOD). We introduce a refined version (CD-NOTS) which is designed
specifically to account for lagged dependencies in time series data. We compare
the performance of different algorithmic choices, such as the type of
conditional independence test and the significance level, to help select the
best hyperparameters given various scenarios of sample size, problem
dimensionality, and availability of computational resources. Using the results
from the simulated data, we apply CD-NOTS to a broad range of real-world
financial applications in order to identify causal connections among
nonstationary time series data, thereby illustrating applications in
factor-based investing, portfolio diversification, and comprehension of market
dynamics.
