Causeval – statistically rigorous, causal evaluation for LLM apps
causeval A statistically rigorous, causal evaluation layer for LLM apps, built on top of DeepEval. Repeated sampling, variance decomposition, clustered-bootstrap confidence intervals, paired comparisons, and statistically valid gates. 🔬 Causality - What caused this output or failure? RAG context ablation and counterfactual context, input perturbations with metamorphic relations, and agent step-level counterfactual replay. ⚖️ Judge validity - Can we trust the judge?
- ▪causeval A statistically rigorous, causal evaluation layer for LLM apps, built on top of DeepEval.
- ▪Repeated sampling, variance decomposition, clustered-bootstrap confidence intervals, paired comparisons, and statistically valid gates. 🔬 Causality - What caused this output or failure?
- ▪RAG context ablation and counterfactual context, input perturbations with metamorphic relations, and agent step-level counterfactual replay. ⚖️ Judge validity - Can we trust the judge?
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,274 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/routsom/causeval |
| Publication time | Fri, 02 Oct 2026 07:32:56 +0000 |
| Retrieval time | 2026-10-02T07:40:46.292Z |
| Last seen | 2026-10-02T07:40:46.292Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | dywv19Z8wFV2 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
causeval A statistically rigorous, causal evaluation layer for LLM apps, built on top of DeepEval. Created and maintained by @routsom. DeepEval measures. It gives you a score. But a single score can't tell you whether it's real (or just noise), why your app produced an output, or whether the LLM judge that produced the score can be trusted. causeval wraps DeepEval's metrics - they stay the measurement instrument - and adds the three things a score alone can't give you: 📊 Uncertainty - Is the score real? Repeated sampling, variance decomposition, clustered-bootstrap confidence intervals, paired comparisons, and statistically valid gates.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.