TECHNOLOGY PROFILE · TECH-0030
Captum
PyTorch model-interpretability library.
Reference link only — not reviewed this edition. This profile preserves the prepared catalog’s original descriptions. Advantages depend on the workload; comparisons are editorial, not benchmark results.
Where it has an edge
Common attribution implementations reduce reimplementation work for gradients, perturbations and influence-style analysis.
Best-fit problems
Explaining supported PyTorch models and testing attribution hypotheses.
Tradeoffs & cautions
Attribution is method-dependent and not necessarily a faithful causal account.
What to test before choosing
Use controlled baselines and counterexamples; test false positives, robustness under shift and validity of conclusions.
Runtime & hardware
Local models or configured APIs
Access & licensing
Open-source; check model/data dependencies
Execution
Local / self-hosted
Lifecycle
Not independently audited
Comparison basis
Editorial problem-fit judgement; no head-to-head benchmark performed
Connected concepts
Alternatives to investigate
SHAP · LIME · TransformerLens