DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data
Causal discovery from unstructured data is a challenging yet underexplored task in high-expertise domains such as healthcare, finance, and education. Existing methods typically leverage the general knowledge of large language models (LLMs) to identify causal factors from unstructured data and annotate them into structured data for causal graph construction. However, they remain limited by two key challenges (CHs): (CH1) insufficient identification of latent factors, which are implicit in the data yet essential for causal discovery, due to the lack of domain-specific knowledge; and (CH2) unreli
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%Anti-Causal Domain Generalization: Leveraging Unlabeled Data - Apple Machine Learning Research →
- PossiblePossibly related (embedding) · 57%cdt15/lingam →
- FuzzySimilar title/name (fuzzy) · 87%Unstructured-IO/unstructured →
“Fuzzy title match (0.94): “DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstru” ≈ “Unstructured-IO/unstructured””
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Wangxin Liu →
“DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data”
- LinkedLinked via arxiv author · 85%Yijin Liu →
“DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data”