Missing Not at Random

Missing Not at Random (MNAR) data, where the probability of missingness depends on the unobserved values, poses a significant challenge in data analysis. Current research focuses on developing robust methods to handle MNAR data, including imputation techniques leveraging deep generative models and inverse probability weighting, often combined with strategies to address noisy or inconsistent data. These advancements aim to improve the accuracy and reliability of analyses across various fields, from recommender systems to medical research, where MNAR data is frequently encountered. The ultimate goal is to enable unbiased inference and reliable model building despite the complexities introduced by MNAR mechanisms.