Multiple Imputation

Multiple imputation addresses the pervasive problem of missing data in datasets by creating multiple plausible imputed datasets, each reflecting the uncertainty inherent in the missing values. Current research focuses on improving imputation accuracy and efficiency, particularly for high-dimensional data, exploring advanced methods like neural networks and incorporating auxiliary information such as natural language processing outputs. These advancements aim to reduce bias and improve the reliability of downstream analyses, impacting diverse fields from healthcare to econometrics by enabling more robust and accurate inferences from incomplete data.