Review for Handling Missing Data with special missing mechanism

Understand

Missing data poses a significant challenge in data science, affecting decision-making processes and outcomes.

• Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data types in the real world.
• Three missing mechanisms are defined in the literature: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR), each presenting unique challenges in imputation.
• Most existing work are focused on MCAR that is relatively easy to handle.