# Explainable Data Imputation using Constraints

arXiv: https://arxiv.org/abs/2205.04731 · HTML: https://mlsanity.com/paper/2205.04731/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 4 of 4)

- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [MIWAE: Deep Generative Modelling and Imputation of Incomplete Data](https://mlsanity.com/paper/1812.02633/) (2018)
- [Missing Value Imputation Based on Deep Generative Models](https://mlsanity.com/paper/1808.01684/) (2018)

## Built on by (most cited first, 3 of 3)

- [Explainability of Machine Learning Models under Missing Data](https://mlsanity.com/paper/2407.00411/) (2024)
- [Conditional expectation with regularization for missing data imputation](https://mlsanity.com/paper/2302.00911/) (2023)
- [Directly Handling Missing Data in Linear Discriminant Analysis for Enhancing Classification Accuracy and Interpretability](https://mlsanity.com/paper/2407.00710/) (2024)

## Abstract (arXiv)

Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analysis methods, such as principle components analysis or singular value decomposition, require complete data. Many approaches impute numeric data and some do not consider dependency of attributes on other attributes, while some require human intervention and domain knowledge. We present a new algorithm for data imputation based on different data type values and their association constraints in data, which are not handled currently by any system. We show experimental results using different metrics comparing our algorithm with state of the art imputation techniques. Our algorithm not only imputes the missing values but also generates human readable explanations describing the significance of attributes used for every imputation.