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  1. Home
  2. Signal Canvas
  3. DISCOVER: A Solver for Distributional Counterfactual Explana
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DISCOVER: A Solver for Distributional Counterfactual Explanations

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Compared to this week’s papers

Stale evidence

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 8

References: 0

Proof: partial

Freshness: stale

Source paper: DISCOVER: A Solver for Distributional Counterfactual Explanations

PDF: https://arxiv.org/pdf/2603.16436v1

Repository: https://github.com/understanding-ml/DCE

Source count: 0

Coverage: 50%

Last proof check: 2026-03-19T20:22:25.550Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

DISCOVER: A Solver for Distributional Counterfactual Explanations

Overall score: 8/10
Lineage: 70ebeb6b1b3f…
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Canonical Paper Receipt

Last verification: 2026-03-19T20:22:25.550Z

Freshness: stale

Proof: partial

Repo: active

References: 0

Sources: 0

Coverage: 50%

Missingness
  • - references
  • - distribution_readiness_scores
  • - paper_extraction_scorecards
Unknowns
  • - distribution readiness has not been computed yet

Mode Notes

  • Corpus mode searches the research corpus broadly.
  • Paper mode pins trust state to the canonical paper kernel.
  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

Starting…

Dimensions overall score 8.0

GitHub Code Pulse

Stars
1
Health
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Last commit
2/12/2026
Forks
0
Open repository

Key claims

Strong 8Mixed 0Weak 0

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Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives
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GCFX: Generative Counterfactual Explanations for Deep Graph Models at the Model Level
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What-If Explanations Over Time: Counterfactuals for Time Series Classification
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Prior Work
xai-cola: A Python library for sparsifying counterfactual explanations
Score 8.0stable
Competing Approach
SCE-LITE-HQ: Smooth visual counterfactual explanations with generative foundation models
Score 7.0down

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Related Resources

  • counterfactual explanations(glossary)
  • How can Mixture of Concept Bottleneck Experts be used for counterfactual explanations in AI?(question)

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