Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis

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.

FOSA: Full Information Maximum Likelihood (FIML) Optimized Self-Attention Imputation for Missing Data

In data imputation, effectively addressing missing values is pivotal, especially in intricate datasets. This paper delves into the FIML Optimized Self-attention (FOSA) framework, an innovative approach that amalgamates the strengths of Full Information Maximum Likelihood (FIML) estimation with the capabilities of self-attention neural networks. Our methodology commences with an initial estimation of missing values via FIML, subsequently refining these estimates by leveraging the self-attention mechanism. Our comprehensive experiments on both simulated and real-world datasets underscore FOSA's pronounced advantages over traditional FIML techniques, encapsulating facets of accuracy, computational efficiency, and adaptability to diverse data structures. Intriguingly, even in scenarios where the Structural Equation Model (SEM) might be mis-specified, leading to suboptimal FIML estimates, the robust architecture of FOSA's self-attention component adeptly rectifies and optimizes the imputation outcomes. Our empirical tests reveal that FOSA consistently delivers commendable predictions, even in the face of up to 40% random missingness, highlighting its robustness and potential for wide-scale applications in data imputation.