Drton group
Generic Identifiability in LiNGAM models with dependent errors
For a given Directed Acyclic Mixed Graph, the Linear Non-Gaussian Acyclic Model (LiNGAM) postulates that each random variable is a linear function of its parents, with exogenous non-Gaussian error terms. In this context, we present a graphical criterion that is both necessary and sufficient for deciding the generic identifiability of a causal effect within a fixed graph. We also provide an algorithm for testing this criterion, which operates in polynomial time relative to the size of the graph. Furthermore, when the graphical criteria are met, we demonstrate that the model parameters can be determined as the solution to an optimization problem, which can be solved using gradient methods.