Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis

arXiv:2404.16361v1 Announce Type: new
Abstract: 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 …

abstract algorithm analysis arxiv causal cs.lg cs.ne cs.sc data data analysis datasets discovery genetic programming impact operators parsing programming relationship research study type variables