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DeciShift v0.2.0 — Trustworthy Decision-Change Evidence

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@github-actions github-actions released this 24 Sep 08:40
v0.2.0
4977542

DeciShift v0.2.0 adds a Trustworthy Decision-Change Evidence layer while preserving the v0.1 public API and CPU-first, local-first design.

Added

  • Streaming Monte Carlo uncertainty for approximate component attribution, including standard errors and configurable confidence intervals.
  • Attribution diagnostics with Shapley efficiency residual checks and warnings.
  • Stable component provenance and strict reproducibility mode.
  • Evidence schema 1.0, deterministic fingerprints, manifests, SHA-256 integrity verification, and decishift verify.
  • User-defined Decision Contracts with deterministic decishift gate exit codes for CI.
  • Stronger record identity, finite-output, row-order, and callable-invocation validation.
  • Dynamic YAML threshold factories/objects while retaining numeric thresholds.
  • Safer cohort discovery with Wilson intervals, coverage, global context, and excess flip rate.
  • Optional binary outcome correctness-transition analysis.
  • Decision Fragility diagnostics for threshold proximity and boundary crossing.
  • Fully local self-contained HTML reports.
  • Saved-run comparison with decishift compare-runs.
  • Expanded benchmarks, regression coverage, Ruff, build, and Twine checks.

Compatibility

Existing v0.1 public imports remain supported, including DecisionPipeline, compare_pipelines, compare_predictions, exact_attribution, approximate_attribution, and pairwise_interactions.

Install

pip install --upgrade decishift==0.2.0
decishift demo

Scientific scope

DeciShift attributes observed software-output changes across a versioned executable decision pipeline. Component attribution is software-counterfactual attribution and does not establish external real-world causal effects. A Decision Contract PASS means only that the user-configured checks passed; it is not proof of safety, fairness, compliance, or correctness.

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