Different Missingness

Missing data is a pervasive problem across numerous scientific domains, hindering accurate analysis and reliable model building. Current research focuses on understanding and mitigating the impact of different missingness mechanisms, ranging from completely random to complex patterns where missingness depends on the unobserved values themselves. This involves developing sophisticated imputation techniques, often leveraging advanced architectures like transformers and diffusion models, and incorporating missingness directly into model learning processes, such as through self-supervised learning or masked autoencoders. Addressing these challenges is crucial for improving the reliability and fairness of analyses across diverse fields, from healthcare to causal inference.