Summary of Linking Model Intervention to Causal Interpretation in Model Explanation, by Debo Cheng et al.
Linking Model Intervention to Causal Interpretation in Model Explanation
by Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 explores the connection between a model’s intuitive explanation of its decision-making process, specifically the “intervention intuition” approach, and its ability to provide a causal interpretation of a feature’s impact on an outcome. The authors investigate the conditions under which this intuitive model intervention effect can be trusted to indicate whether a feature is a direct cause of the outcome. They also highlight the limitations of using such an approach in environments with unobserved features. To validate their findings, the researchers conducted experiments on semi-synthetic datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about understanding how machine learning models make decisions and why they are trustworthy or not. It looks at a special way to explain how a model works, called “intervention intuition,” which shows what would happen if you changed a certain feature. The authors want to know when this explanation can tell us if a feature really causes something to happen. They also discuss the limits of using this approach when there are things we don’t observe. To test their ideas, they created fake datasets that combine real and made-up data. |
Keywords
* Artificial intelligence * Machine learning
