This repository contains the research code for

Vaidotas Simkus, Michael U. Gutmann. CFMI: Flow Matching for Missing Data Imputation. arXiv, 2025.

The code is shared for reproducibility purposes and is not intended for production use.

We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation. Our comparison with nine classical and state-of-the-art imputation methods on 24 small to moderate-dimensional tabular data sets shows that CFMI matches or outperforms both traditional and modern techniques across a wide range of metrics. Applying the method to zero-shot imputation of time-series data, we find that it matches the accuracy of a related diffusion-based method while outperforming it in terms of computational efficiency. Overall, CFMI performs at least as well as traditional methods on lower-dimensional data while remaining scalable to high-dimensional settings, matching or exceeding the performance of other deep learning-based approaches, making it a go-to imputation method for a wide range of data types and dimensionalities.

Install python dependencies from conda and the cfmi project package with

conda env create -f environment.yml
conda activate cfmi
python setup.py develop

If the dependencies in environment.yml change, update dependencies with

conda env update --file environment.yml