# Missing Data Imputation using Neural Cellular Automata

## TL;DR

This paper introduces a novel missing data imputation method using Neural Cellular Automata, demonstrating superior performance over existing models in accuracy and post-imputation tasks.

## Contribution

The paper pioneers the application of Neural Cellular Automata for missing data imputation, adapting the model specifically for this task.

## Key findings

- NCA-based model outperforms state-of-the-art imputation methods.
- The proposed method reduces imputation error significantly.
- Post-imputation performance is improved with the NCA approach.

## Abstract

When working with tabular data, missingness is always one of the most painful problems. Throughout many years, researchers have continuously explored better and better ways to impute missing data. Recently, with the rapid development evolution in machine learning and deep learning, there is a new trend of leveraging generative models to solve the imputation task. While the imputing version of famous models such as Variational Autoencoders or Generative Adversarial Networks were investigated, prior work has overlooked Neural Cellular Automata (NCA), a powerful computational model. In this paper, we propose a novel imputation method that is inspired by NCA. We show that, with some appropriate adaptations, an NCA-based model is able to address the missing data imputation problem. We also provide several experiments to evidence that our model outperforms state-of-the-art methods in terms of imputation error and post-imputation performance.