Chang Deng

Causal structure learning built on differentiable acyclicity constraints has lacked finite-sample theory for nonlinear models. This work sets each node's conditional distribution to a generalized linear model, searches all topological orders through one continuous objective, and proves that global minimizers select the sparsest representation at the population level and attain an estimation error rate up to log factors at the sample level. Experiments examine structure recovery accuracy and runtime on logistic, Poisson and Gaussian DAGs.

Causal structure learning built on differentiable acyclicity constraints has lacked finite-sample theory for nonlinear models. This work sets each node's conditional distribution to a generalized linear model, searches all topological orders through one continuous objective, and proves that global minimizers select the sparsest representation at the population level and attain an estimation error rate up to log factors at the sample level. Experiments examine structure recovery accuracy and runtime on logistic, Poisson and Gaussian DAGs.