CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation

We present CACTI, a masked autoencoding approach for imputing tabular data that leverages the structure in missingness patterns and contextual information. Our approach employs a novel median truncated copy masking training strategy that encourages the model to learn from empirical patterns of missingness while incorporating semantic relationships between features -captured by column names and text descriptions -to better represent feature dependence. These dual sources of inductive bias enable CACTI to outperform state-of-the-art methods -an average R 2 gain of 7.8% over the next best method (13.4%, 6.1%, and 5.3% under missing not at random, at random and completely at random, respectively)across a diverse range of datasets and missingness conditions. Our results highlight the value of leveraging dataset-specific contextual information and missingness patterns to enhance imputation performance. Code is publicly available at github.com/sriramlab/CACTI A primary reason underlying this challenge is that missing-