Tag
A paper on learning causal structure in linear Gaussian structural causal models with directed cycles and latent confounders, introducing marginal quasi-equivalence and a differentiable Bernoulli-gate parameterization with a consistent complexity penalty.
This paper analyzes how Bayesian causal discovery fails under latent confounding in linear Gaussian networks, deriving a correlation threshold that causes spurious edges to be favored by the score function and characterizing two distinct posterior failure regimes.
Proposes a finite-sample method for recovering the sparsest DAG in linear non-Gaussian acyclic models with latent confounders using higher-order cumulants, without restricting the number of latents.
FoundCause is an amortized causal discovery model that explicitly handles latent confounders and missing data, outperforming 15 existing methods on real-world datasets with a single forward pass.