  On time-series data, most causal discovery methods fit a new model whenever
they encounter samples from a new underlying causal graph. However, these
samples often share relevant information which is lost when following this
approach. Specifically, different samples may share the dynamics which describe
the effects of their causal relations. We propose Amortized Causal Discovery, a
novel framework that leverages such shared dynamics to learn to infer causal
relations from time-series data. This enables us to train a single, amortized
model that infers causal relations across samples with different underlying
causal graphs, and thus leverages the shared dynamics information. We
demonstrate experimentally that this approach, implemented as a variational
model, leads to significant improvements in causal discovery performance, and
show how it can be extended to perform well under added noise and hidden
confounding.
