MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
This addresses the need for researchers and practitioners to study missing data mechanisms in heterogeneous tabular data, though it is incremental as it consolidates existing functionalities into a single tool.
The authors tackled the problem of fragmented and limited tools for simulating missing data in real-world datasets by developing MissMecha, an all-in-one Python package that supports numerical and categorical features under MCAR, MAR, and MNAR assumptions, providing a unified platform for simulation, visualization, and evaluation.
Incomplete data is a persistent challenge in real-world datasets, often governed by complex and unobservable missing mechanisms. Simulating missingness has become a standard approach for understanding its impact on learning and analysis. However, existing tools are fragmented, mechanism-limited, and typically focus only on numerical variables, overlooking the heterogeneous nature of real-world tabular data. We present MissMecha, an open-source Python toolkit for simulating, visualizing, and evaluating missing data under MCAR, MAR, and MNAR assumptions. MissMecha supports both numerical and categorical features, enabling mechanism-aware studies across mixed-type tabular datasets. It includes visual diagnostics, MCAR testing utilities, and type-aware imputation evaluation metrics. Designed to support data quality research, benchmarking, and education,MissMecha offers a unified platform for researchers and practitioners working with incomplete data.