# Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals

arXiv: https://arxiv.org/abs/2407.20047 · HTML: https://mlsanity.com/paper/2407.20047/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 29 Jul 2024.

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

- [Handling Missing Data with Graph Representation Learning](https://mlsanity.com/paper/2010.16418/) (2020)
- [Missing Data Imputation with Adversarially-trained Graph Convolutional Networks](https://mlsanity.com/paper/1905.01907/) (2019)
- [Conformal Prediction with Missing Values](https://mlsanity.com/paper/2306.02732/) (2023)
- [Generative Machine Learning for Multivariate Equity Returns](https://mlsanity.com/paper/2311.14735/) (2023)

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

- [Advancing ESG Intelligence: An Expert-level Agent and Comprehensive Benchmark for Sustainable Finance](https://mlsanity.com/paper/2601.08676/) (2026)

## Abstract (arXiv)

Environmental, Social, and Governance (ESG) datasets are frequently plagued by significant data gaps, leading to inconsistencies in ESG ratings due to varying imputation methods. This paper explores the application of established machine learning techniques for imputing missing data in a real-world ESG dataset, emphasizing the quantification of uncertainty through prediction intervals. By employing multiple imputation strategies, this study assesses the robustness of imputation methods and quantifies the uncertainty associated with missing data. The findings highlight the importance of probabilistic machine learning models in providing better understanding of ESG scores, thereby addressing the inherent risks of wrong ratings due to incomplete data. This approach improves imputation practices to enhance the reliability of ESG ratings.