  Causal models can compactly and efficiently encode the data-generating
process under all interventions and hence may generalize better under changes
in distribution. These models are often represented as Bayesian networks and
learning them scales poorly with the number of variables. Moreover, these
approaches cannot leverage previously learned knowledge to help with learning
new causal models. In order to tackle these challenges, we represent a novel
algorithm called \textit{causal relational networks} (CRN) for learning causal
models using neural networks. The CRN represent causal models using continuous
representations and hence could scale much better with the number of variables.
These models also take in previously learned information to facilitate learning
of new causal models. Finally, we propose a decoding-based metric to evaluate
causal models with continuous representations. We test our method on synthetic
data achieving high accuracy and quick adaptation to previously unseen causal
models.
