# Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

@inproceedings{Lwe2022AmortizedCD, title={Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data}, author={Sindy L{\"o}we and David Madras and Richard S. Zemel and Max Welling}, booktitle={CLeaR}, year={2022} }

Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information - for instance, the dynamics describing the effects of causal relations - which is lost when following this approach. 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…

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