  We present Causal Generative Neural Networks (CGNNs) to learn functional
causal models from observational data. CGNNs leverage conditional
independencies and distributional asymmetries to discover bivariate and
multivariate causal structures. CGNNs make no assumption regarding the lack of
confounders, and learn a differentiable generative model of the data by using
backpropagation. Extensive experiments show their good performances
comparatively to the state of the art in observational causal discovery on both
simulated and real data, with respect to cause-effect inference, v-structure
identification, and multivariate causal discovery.
