evo-trend-anomaly-causal-inference
De la autor
Complete pipeline for identifying anomalous product category spending patterns in e-commerce data using Prophet counterfactual forecasting, and explaining anomalies through demographic factors via Difference-in-Differences causal inference.
Instalare
npx skills add OpenLAIR/OpenSkill --skill evo-trend-anomaly-causal-inference -a claude-code
Se instalează în .claude/skills/evo-trend-anomaly-causal-inference/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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De ce 75
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
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- investigating-anomalous-resultsK-Dense-AI/science-superpowersUse when a result is surprising, impossible, contradicts a sanity check, a pipeline fails, a model won't converge, or a replication fails - before adjusting anythingFinanțe★ 362
- anomaly-investigationgaasher/Agent-Loop-SkillsUse when the user has a known, already-observed anomaly in their data — a metric spike or drop, an outlier, an unexpected number — and wants its root cause diagnosed, not guessed. Forms a slate of candidate causes, tests each against the data, and eliminates the ones the data refutes, narrowing the live candidates until exactly one survives refutation and passes a positive confirming test. The result is an investigation log with the confirmed root cause and the evidence that ruled out the alternatives. Not for open-ended discovery over a dataset with no specific anomaly in hand (that is data-analysis), and not for checking an external claim against sources (that is claim-verify) — this is reactive diagnosis of one anomaly you already know about.Finanțe★ 177
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