MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
Abstract
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.
Keywords
Cite
@article{arxiv.2508.04740,
title = {MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms},
author = {Youran Zhou and Mohamed Reda Bouadjenek and Sunil Aryal},
journal= {arXiv preprint arXiv:2508.04740},
year = {2025}
}