# MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms

arXiv: https://arxiv.org/abs/2508.04740 · HTML: https://mlsanity.com/paper/2508.04740/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [Missing Data Imputation using Optimal Transport](https://mlsanity.com/paper/2002.03860/) (2020)
- [Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques](https://mlsanity.com/paper/2210.08258/) (2022)
- [HI-PMK: A Data-Dependent Kernel for Incomplete Heterogeneous Data Representation](https://mlsanity.com/paper/2501.04300/) (2025)
- [MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation](https://mlsanity.com/paper/2508.03083/) (2025)
- [Developing robust methods to handle missing data in real-world applications effectively](https://mlsanity.com/paper/2502.19635/) (2025)

## Built on by (most cited first, 1 of 1)

- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)

## Abstract (arXiv)

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.