Summary of Mcce: Missingness-aware Causal Concept Explainer, by Jifan Gao et al.
MCCE: Missingness-aware Causal Concept Explainer
by Jifan Gao, Guanhua Chen
First submitted to arxiv on: 14 Nov 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 introduces Missingness-aware Causal Concept Explainer (MCCE), a novel framework for estimating the causal effects of human-understandable concepts on machine learning model outputs. This approach is essential for interpretable machine learning, as it explains complex behaviors by linking high-level knowledge to model outputs. However, existing methods assume complete observation of all concepts, which is often unrealistic due to incomplete annotations or missing concept data. MCCE addresses this limitation by learning to account for residual bias resulting from unobserved concepts and utilizing a linear predictor to model the relationships between these concepts and model outputs. The framework can provide both local and global explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes machine learning more understandable by creating a new way to explain how high-level ideas affect what a computer learns. This is important because it helps people understand why computers make certain decisions. However, most current methods assume that we have all the information about these high-level ideas, which isn’t always true. The new approach, called MCCE, takes this into account and provides better explanations. |
Keywords
* Artificial intelligence * Machine learning
