Summary of Denoising Esg: Quantifying Data Uncertainty From Missing Data with Machine Learning and Prediction Intervals, by Sergio Caprioli et al.
Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals
by Sergio Caprioli, Jacopo Foschi, Riccardo Crupi, Alessandro Sabatino
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the application of established machine learning techniques for imputing missing data in an Environmental, Social, and Governance (ESG) dataset. The authors emphasize the quantification of uncertainty through prediction intervals using multiple imputation strategies. They assess the robustness of imputation methods and quantify the uncertainty associated with missing data. The study highlights the importance of probabilistic machine learning models in providing a 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to fix gaps in Environmental, Social, and Governance (ESG) data sets. Right now, different methods are used to fill in missing information, which can lead to inconsistent ESG scores. The authors use machine learning techniques to impute missing data and quantify the uncertainty of their results. They test multiple methods and see how they perform. This study shows that using probabilistic models helps us better understand ESG ratings and reduces the risk of wrong ratings due to incomplete data. |
Keywords
» Artificial intelligence » Machine learning
