Missing Value Imputation

Missing value imputation aims to replace missing data points in datasets, enabling the use of otherwise unusable data for analysis and modeling. Current research emphasizes developing robust imputation methods that handle various missing data mechanisms (e.g., Missing Not at Random), incorporating feature importance and inter-variable relationships (using techniques like graph neural networks, Bayesian networks, and deep learning models such as variational autoencoders and MLPs), and evaluating imputation techniques holistically considering predictive performance, fairness, and stability. Effective imputation is crucial for improving the accuracy and reliability of analyses across diverse fields, from environmental monitoring and biomedical research to machine learning applications.