Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposals

2026-08-21

AI Summary

该研究通过结合大型语言模型和实证数据评分的神经符号框架，生成关于不良妊娠结局的合理因果假设，以解决数据稀缺和领域知识不完整的问题。

Abstract

Adverse Pregnancy Outcomes (APOs) such as preterm birth and gestational diabetes can have long-term consequences for both the mother and child, yet an understanding of their causes remains elusive. Causal discovery in this domain is especially challenging due to a paucity of data and incomplete domain knowledge. As a result, pure data-driven methods fail, and Large Language Model (LLM) outputs remain inconsistent or contradictory. We introduce a neurosymbolic framework for generating plausible causal hypotheses that iteratively combines the broad prior knowledge of LLMs with empirical scoring on data. Our method treats the LLM as an adaptive proposal distribution, generating hypotheses that are scored against empirical data; the resulting high-scoring graphs are then used to update the LLM's context, steering subsequent generations toward more promising regions of the hypothesis space. We evaluate our approach on a real-world clinical dataset for modeling APOs and their risk factors, comparing our results against an expert-constructed causal graph. Our method recovers all expert-validated edges and identifies additional plausible causal relations not previously listed by experts, potentially providing new insights for targeted interventions.

Problem

• Adverse Pregnancy Outcomes
• Causal Discovery
• Data Scarcity
• Incomplete Domain Knowledge

Innovation

• neurosymbolic framework
• adaptive LLM proposals
• causal discovery
• adverse pregnancy outcomes
• empirical scoring