Learning Latent Causal Structures with a Redundant Input Neural Network

no code implementations • 29 Mar 2020 • Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu

We developed a deep learning model, which we call a redundant input neural network (RINN), with a modified architecture and a regularized objective function to find causal relationships between input, hidden, and output variables.

Causal Discovery