Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks

9 Oct 2019Julius von KügelgenPaul K RubensteinBernhard SchölkopfAdrian Weller

We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform those experiments which, in expectation with respect to the current model, are maximally informative about the underlying causal structure... (read more)

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