Summary of Distributional Counterfactual Explanations with Optimal Transport, by Lei You et al.
Distributional Counterfactual Explanations With Optimal Transport
by Lei You, Lele Cao, Mattias Nilsson, Bo Zhao, Lei Lei
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (stat.ML)
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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 Distributional Counterfactual Explanation (DCE), which shifts the focus from specific input modifications to capturing nuanced distributional characteristics that influence model outcomes across the entire input-output spectrum. Unlike existing CE approaches, DCE uses optimal transport (OT) to formulate a chance-constrained optimization problem, deriving a counterfactual distribution aligned with its factual counterpart, supported by statistical confidence. This approach has the potential to provide deeper insights into decision-making models and is particularly beneficial for stakeholders making strategic decisions based on statistical data analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers make decisions by showing us what would have happened if things had been different. Right now, we can only see how computers change when we change one thing at a time. But this new method looks at all the ways things could be different and shows us how that would affect the computer’s decision. This is important because it helps people understand why computers are making certain decisions and make better choices themselves. |
Keywords
* Artificial intelligence * Optimization
