  In this paper, we propose a score-based normalizing flow method called DAG-NF
to learn dependencies of input observation data. Inspired by Grad-CAM in
computer vision, we use jacobian matrix of output on input as causal
relationships and this method can be generalized to any neural networks
especially for flow-based generative neural networks such as Masked
Autoregressive Flow(MAF) and Continuous Normalizing Flow(CNF) which compute the
log likelihood loss and divergence of distribution of input data and target
distribution. This method extends NOTEARS which enforces a important acylicity
constraint on continuous adjacency matrix of graph nodes and significantly
reduce the computational complexity of search space of graph.
