Causal Generative Neural Networks
This work addresses causal discovery from observational data for researchers in machine learning and statistics, representing a novel method for a known bottleneck.
The authors tackled the problem of learning functional causal models from observational data by introducing Causal Generative Neural Networks (CGNNs), which achieved good performance in cause-effect inference, v-structure identification, and multivariate causal discovery compared to state-of-the-art methods on simulated and real data.
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.