Summary of Discret: Synthesizing Faithful Explanations For Treatment Effect Estimation, by Yinjun Wu et al.
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
by Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J Getzen, Qi Long, Mayur Naik, Ravi B Parikh, Eric Wong
First submitted to arxiv on: 2 Jun 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 This paper proposes a novel framework, called DISCRET, for individual treatment effect estimation (ITE) in AI models. The goal is to design faithful yet accurate models that provide explanations, which is crucial in critical settings like healthcare. Existing solutions are inadequate as state-of-the-art black-box models don’t supply explanations, post-hoc explainers lack faithfulness guarantees, and self-interpretable models compromise accuracy. DISCRET synthesizes rule-based explanations for each sample, leveraging a novel RL algorithm to efficiently search the large explanation space. The framework is evaluated on diverse tasks involving tabular, image, and text data, outperforming best self-interpretable models while providing faithful explanations comparable to black-box models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to create accurate AI models that can explain their decisions. This is important for making good choices in situations like healthcare where the consequences are serious. Right now, there are problems with how AI models work: some don’t give reasons why they made certain choices, while others provide explanations but aren’t very reliable. The authors of this paper created a new framework called DISCRET that solves these issues by providing accurate and trustworthy explanations for each individual case. This is done using a special algorithm that helps the model find the best explanation from a large group of possibilities. |
