Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
By Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller. 2019
22 citations recorded by the source corpus.
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. Unlike previous work, we consider the setting of continuous random variables with non-linear functional relationships, modelled with Gaussian process priors. To address the arising problem of choosing from an uncountable set of possible interventions, we propose to use Bayesian optimisation to efficiently maximise a Monte Carlo estimate of the expected information gain.
Subfields: Artificial Intelligence, Management Science and Operations Research
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In Artificial Intelligence
- Bernhard Schölkopf
- Julius von Kügelgen
- Amir-Hossein Karimi
- Isabel Valera
- Dominik Janzing
- Arthur Gretton
- Kenji Fukumizu
- Jan Peters
In Management Science and Operations Research
Citations in the corpus
- Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “While GP s have previously been studied in a causal context for structure learning [10, 45], estimating treatment effects [2, 34], or learning SCM s with latent variables and measurement error [38], our goal here is to account for the uncertainty over f r in the computation of the posterior over U…”