  This study proposes Evolutionary Causal Discovery (ECD) for causal discovery
that tailors response variables, predictor variables, and corresponding
operators to research datasets. Utilizing genetic programming for variable
relationship parsing, the method proceeds with the Relative Impact
Stratification (RIS) algorithm to assess the relative impact of predictor
variables on the response variable, facilitating expression simplification and
enhancing the interpretability of variable relationships. ECD proposes an
expression tree to visualize the RIS results, offering a differentiated
depiction of unknown causal relationships compared to conventional causal
discovery. The ECD method represents an evolution and augmentation of existing
causal discovery methods, providing an interpretable approach for analyzing
variable relationships in complex systems, particularly in healthcare settings
with Electronic Health Record (EHR) data. Experiments on both synthetic and
real-world EHR datasets demonstrate the efficacy of ECD in uncovering patterns
and mechanisms among variables, maintaining high accuracy and stability across
different noise levels. On the real-world EHR dataset, ECD reveals the
intricate relationships between the response variable and other predictive
variables, aligning with the results of structural equation modeling and
shapley additive explanations analyses.
