Publikation
Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposals
Kavimayil P. Komarasamy; Saurabh Mathur; Ameet Soni; David Haase; Kristian Kersting; Sriraam Natarajan
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2608.21079, Pages 1-9, arXiv, 2026.
Zusammenfassung
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 distri-
bution, 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.