Summary of Still More Shades Of Null: An Evaluation Suite For Responsible Missing Value Imputation, by Falaah Arif Khan et al.
Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation
by Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv, Julia Stoyanovich
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 This paper proposes an evaluation suite called Shades-of-Null to assess responsible missing value imputation. The authors create novel scenarios for missingness, including multi-mechanism missingness and missingness shift, which go beyond traditional settings like Missing Completely at Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). They also evaluate imputers based on imputation quality, imputation fairness, predictive performance, fairness, and stability. The paper aims to address the practical challenge of data missingness in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to deal with missing data that is fair and good for everyone. It’s like trying to fix a puzzle where some pieces are missing. The authors created new scenarios for missing data, like when different patterns of missingness happen together or when the rules change between training and testing. They also checked how well imputation works by looking at how well it helps models make predictions, whether it’s fair, and if it stays stable. |
