NICE: an algorithm for nearest instance counterfactual explanations
Results and benchmarks
NICE: an algorithm for nearest instance counterfactual explanations presents a k-nearest neighbors algorithm approach for counterfactual thinking.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: What we imagine versus how we imagine, and a problem for explaining counterfactual thoughts with causal ones.
- Start from this likely method family: k-nearest neighbors algorithm.
Time to first repro: a few days
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- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
No verified implementation available
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Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Hugging Face artifacts
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Research context
46
Citations
76
References
Tasks
Counterfactual thinking, Counterfactual conditional, Computer science, Nice, Feature (linguistics), Exploit, Differentiable function, Process (computing)
Methods
k-nearest neighbors algorithm, Algorithm
Domains
Machine learning, Artificial intelligence
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