Causeval is a statistical evaluation layer for LLM applications built on DeepEval that adds uncertainty quantification, causal analysis, and judge validity checks to metric scores. It provides confidence intervals, variance decomposition, ablation studies, and bias audits to enable defensible decisions about LLM app performance.
Partial pooling through hierarchical and multilevel models resolves the reference class problem by combining information across different levels of grouping rather than selecting a single reference class. The approach automatically balances bias and variance by shrinking group estimates toward population means based on data, eliminating the need for philosophers to debate which comparison group is correct.
Three deaths occurred at Burning Man in 2026, the highest on record for the event. Comparing against US population death rates, the crude rate would predict 12.2 deaths, but age-standardized analysis accounting for Burning Man's younger, healthier demographic suggests 4.9 expected deaths, making the observed three deaths statistically unremarkable.
An interactive educational tool that teaches fundamental statistics concepts, demonstrating how random sampling and the central limit theorem transform observations into statistical estimates.