Wednesday 16 April 2025
As data continues to play a crucial role in various fields, researchers have been working to develop methods that can effectively handle missing data. A recent study published in a leading scientific journal explores the concept of domain adaptation under missingness not at random (MNAR), which is a type of missing data problem where the probability of missingness depends on both the observed and unobserved variables.
Domain adaptation refers to the process of adapting a machine learning model trained on one dataset, known as the source domain, to another dataset, called the target domain. This technique has been widely used in various applications, such as image recognition, natural language processing, and recommender systems. However, most existing methods assume that the missing data mechanism is either missing at random (MAR) or ignorable.
In contrast, MNAR refers to a more complex scenario where the missingness pattern is not independent of both the observed and unobserved variables. This can occur when the probability of missingness depends on the value of the missing variable itself, making it challenging for traditional methods to accurately impute the missing data.
Researchers have proposed several approaches to handle MNAR missing data, including joint Bayesian models and machine learning-based methods like Not-MIWAE and GINA. These methods aim to model the relationship between the observed variables, the missingness mechanism, and the outcome variable. However, these approaches often require strong assumptions about the underlying data generating process.
The recent study takes a different approach by reducing the problem of domain adaptation under MNAR to an imputation problem. The authors show that by combining modern MNAR missingness imputation techniques with classic tools from the domain adaptation literature, they can develop a novel procedure for adapting a machine learning model trained on source data to target data.
The study uses a simulation-based approach to evaluate the performance of various imputation models under different scenarios. The results suggest that while traditional methods perform reasonably well in certain settings, more advanced approaches like Not-MIWAE and GINA can provide better accuracy when dealing with complex missing data mechanisms.
Furthermore, the study highlights the importance of considering both the covariate shift and missingness shift problems when adapting a machine learning model to a new domain. The authors demonstrate that by modeling the relationship between the observed variables, the missingness mechanism, and the outcome variable, they can develop a more effective approach for handling MNAR missing data.
Cite this article: “Unlocking Hidden Patterns: Advances in Domain Adaptation for Missing Not at Random Data”, The Science Archive, 2025.
Machine Learning, Domain Adaptation, Missing Data, Not-Miwae, Gina, Mnar, Bayesian Models, Imputation, Simulation-Based Approach, Covariate Shift
