Causal discovery from observational data holds great promise, but existing methods rely on strong assumptions about the underlying causal structure, often requiring full observability of all relevant variables. We tackle these challenges by leveraging the score function ∇ log p(X) of observed variables for causal discovery and propose the following contributions. First, we fine-tune the existing identifiability
