  Time series data are found in many areas of healthcare such as medical time
series, electronic health records (EHR), measurements of vitals, and wearable
devices. Causal discovery, which involves estimating causal relationships from
observational data, holds the potential to play a significant role in
extracting actionable insights about human health. In this study, we present a
novel constraint-based causal discovery approach for autocorrelated and
non-stationary time series data (CDANs). Our proposed method addresses several
limitations of existing causal discovery methods for autocorrelated and
non-stationary time series data, such as high dimensionality, the inability to
identify lagged causal relationships, and overlooking changing modules. Our
approach identifies lagged and instantaneous/contemporaneous causal
relationships along with changing modules that vary over time. The method
optimizes the conditioning sets in a constraint-based search by considering
lagged parents instead of conditioning on the entire past that addresses high
dimensionality. The changing modules are detected by considering both
contemporaneous and lagged parents. The approach first detects the lagged
adjacencies, then identifies the changing modules and contemporaneous
adjacencies, and finally determines the causal direction. We extensively
evaluated our proposed method on synthetic and real-world clinical datasets,
and compared its performance with several baseline approaches. The experimental
results demonstrate the effectiveness of the proposed method in detecting
causal relationships and changing modules for autocorrelated and non-stationary
time series data.
