Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding
未观测混杂下线性非高斯无环模型的可微因果发现
We propose a novel score-based causal discovery method, named ABIC LiNGAM, which extends the linear non-Gaussian acyclic model (LiNGAM) framework to address the challenges of causal structure estimation in scenarios involving unmeasured confounders. By introducing the assumption that error terms follow a multivariate generalized normal distribution, our method leverages continuous optimization techniques to recover acyclic directed mixed graphs (ADMGs), including causal…