Unverified50% confidenceFactExact time
Causal foundation models (CFMs) perform impressively on unit-level treatment effect estimation, but their confidence intervals for group-level average effects are severely miscalibrated.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
Unverified研究表明清晰的系统级约定能将模型产生幻觉或破坏性操作的概率降低 30%~50%65% similarUnverified效应量的判断标准是：0.2属于小效应，0.5属于中等效应，0.8以上算大效应65% similarUnverified功效分析需要预先指定显著性水平α（通常0.05）、期望统计功效（通常0.8或0.9）和预期效应量三个核心参数64% similarUnverifiedThe input features of churn prediction models include RFM metrics, namely Recency (time since last use), Frequency (usage frequency), and Monetary (spending amount).64% similarUnverifiedThe average evaluation metrics of a new LLM may be better than those of an older model, but regressions can occur in niche critical scenarios.63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1068319API
curl https://kongchang.com/api/v1/knowledge/claims/1068319MCP
get_claim(id=1068319)