Summary of Causal Generative Explainers Using Counterfactual Inference: a Case Study on the Morpho-mnist Dataset, by Will Taylor-melanson and Zahra Sadeghi and Stan Matwin
Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset
by Will Taylor-Melanson, Zahra Sadeghi, Stan Matwin
First submitted to arxiv on: 21 Jan 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 proposes using causal generative learning as an interpretable tool to explain image classifiers. It presents a method called generative counterfactual inference, which studies the influence of visual features and causal factors through generative learning. The approach involves uncovering influential pixels by varying causal attribute values and computing explanations for counterfactual images. The paper also establishes a Monte-Carlo mechanism to adapt Shapley explainers for human-interpretable attributes in causal datasets. Finally, it presents optimization methods for creating counterfactual explanations of classifiers using counterfactual inference, which can be applied to differentiable or arbitrary classifiers. The proposed methods are evaluated on the Morpho-MNIST causal dataset and compared with OmnixAI’s visual explanation methods, showing that they provide more interpretable explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to explain how image classifiers work. It uses a special kind of learning called generative counterfactual inference to figure out which parts of an image are most important for making a prediction. The method involves changing certain parts of the image and seeing how that affects the classifier’s decision. The paper also develops a new way to adapt this approach to work with causal datasets, where the goal is to understand why certain images were labeled in a particular way. The proposed methods are tested on a dataset called Morpho-MNIST and compared with another method from OmnixAI. The results show that the new method provides more understandable explanations. |
Keywords
* Artificial intelligence * Inference * Optimization
