Summary of Natural Counterfactuals with Necessary Backtracking, by Guang-yuan Hao et al.
Natural Counterfactuals With Necessary Backtracking
by Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); 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 In this paper, researchers propose a novel framework for generating more feasible counterfactual scenarios by incorporating backtracking when needed. The approach aims to minimize deviations from realistic scenarios while still providing explanations and making decisions. Building on Judea Pearl’s influential work, the methodology controls the extent of backtracking using a naturalness criterion. Experimental results demonstrate the effectiveness of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out what would have happened if your favorite sports team had won last week’s game. You want to know how things would be different today if they had won, but you also don’t want it to sound completely unrealistic. This paper is about creating a new way to do this kind of thinking with computers. It helps by allowing the computer to “backtrack” and make small changes to what happened earlier, so that the outcome still feels realistic. |
