做生物医学文本挖掘或因果推理的团队终于有了一个高质量、细粒度的标注资源，可以直接用来训练和评估模型，建议点开看看数据细节。
因果抽取是生物医学文本挖掘的核心任务，但现有资源常混淆因果与关联、局限于句子级标注或仅关注显式因果线索。PubMedCausal 是一个基于 PubMed 摘要构建的跨度级标注语料库，包含 3 万段落级样本、3945 条因果行和 6491 个因果对，标注了完整因果跨度、类型及句子属性。基准测试显示，生物医学编码器 PubMedBERT 在因果检测上 F1 达 0.7391，而 DeepSeek-R1-32B 在跨度级抽取上 F1 为 0.6765。该语料库支持跨数据集评估，并揭示了类别不平衡、长因果跨度、隐式因果等挑战。
PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text
Causal relation extraction (CRE) is central to biomedical text mining, but current resources often conflate causal relations with broader associations, restrict annotation to sentence-level examples, or focus mainly on explicit causal cues. This limits their usefulness for evaluating whether models can recover causal claims as they are actually expressed in biomedical text. We introduce PubMedCausal, a span-level annotated corpus for biomedical CRE built from PubMed abstracts. The corpus contains 30,000 paragraph-level rows, including 3,945 causal rows and 6,491 adjudicated cause--effect pairs. Each causal relation is annotated with full-text cause and effect spans, causality type, and sententiality, enabling evaluation of both causal detection and full-span causal extraction. We benchmark discriminative encoders and open-source generative models across detection and extraction settings. For causal detection, biomedical encoders are strongest, with PubMedBERT reaching an F$_1$ score of 0.7391. For span-level extraction, the best generative baseline is DeepSeek-R1-32B with few-shot prompting, reaching a Cosine Pair F$_1$ of 0.6765. We further test transfer learning by evaluating PubMedCausal-trained encoders on external causal relation datasets, showing that the resource supports cross-dataset evaluation. Our results show that biomedical CRE remains difficult under class imbalance, long causal spans, implicit causality, inter-sentential relations, and prompt sensitivity. Code and Data can be found here: https://github.com/josiahpaul07/PubMedCausal_Exp