# MIMICause: Representation and automatic extraction of causal relation types from clinical notes

arXiv: https://arxiv.org/abs/2110.07090 · HTML: https://mlsanity.com/paper/2110.07090/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Causal BERT : Language models for causality detection between events expressed in text](https://mlsanity.com/paper/2012.05453/) (2020)

## Built on by (most cited first, 11 of 11)

- [RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media](https://mlsanity.com/paper/2210.06331/) (2022)
- [CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models](https://mlsanity.com/paper/2210.04191/) (2022)
- [Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMs](https://mlsanity.com/paper/2504.10936/) (2025)
- [Prompting or Fine-tuning? Exploring Large Language Models for Causal Graph Validation](https://mlsanity.com/paper/2406.16899/) (2024)
- [Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-based Causal Discovery](https://mlsanity.com/paper/2407.18752/) (2024)
- [SA-MDKIF: A Scalable and Adaptable Medical Domain Knowledge Injection Framework for Large Language Models](https://mlsanity.com/paper/2402.00474/) (2024)
- [Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery](https://mlsanity.com/paper/2506.08771/) (2025)
- [Template Filling for Controllable Commonsense Reasoning](https://mlsanity.com/paper/2111.00539/) (2021)
- [Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry](https://mlsanity.com/paper/2411.10172/) (2024)
- [Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension](https://mlsanity.com/paper/2503.01302/) (2025)
- [PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text](https://mlsanity.com/paper/2605.28363/) (2026)

## Abstract (arXiv)

Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combined with structured EHR data such as patients' demographics, diagnoses, and medications. This will enhance healthcare providers' ability to identify aspects of a patient's story communicated in the clinical notes and help make more informed decisions. In this work, we propose annotation guidelines, develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts in clinical notes; communicated implicitly or explicitly, identified either in a single sentence or across multiple sentences. We annotate a total of 2714 de-identified examples sampled from the 2018 n2c2 shared task dataset and train four different language model based architectures. Annotation based on our guidelines achieved a high inter-annotator agreement i.e. Fleiss' kappa (kappa) score of 0.72, and our model for identification of causal relations achieved a macro F1 score of 0.56 on the test data. The high inter-annotator agreement for clinical text shows the quality of our annotation guidelines while the provided baseline F1 score sets the direction for future research towards understanding narratives in clinical texts.