AmortizedCausalDiscovery
Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
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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 that infers causal relations across samples with different underlying causal graphs, and thus makes use of the information that is shared. 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 hidden confounding.
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Causal structure discovery in complex dynamical systems is an important
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A very important topic in systems biology is developing statistical meth...
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This paper focuses on causal structure estimation from time series data ...
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We address in this study the problem of learning a summary causal graph ...
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Going beyond correlations, the understanding and identification of causa...
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Causal discovery, i.e., inferring underlying cause-effect relationships ...
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Inferring causal relations from observational time series data is a key
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