Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
arXiv:1910.03962
Abstract
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.
arXiv commentWorking paper. Accepted as a poster at the NeurIPS 2019 workshop, "Do the right thing": machine learning and causal inference for improved decision making. (6 pages + references + appendix)
References in corpus (9)
- Distinguishing cause from effect using observational data: methods and benchmarks
- Causal Discovery from a Mixture of Experimental and Observational Data
- Two Optimal Strategies for Active Learning of Causal Models from Interventional Data
- Causal Discovery from Changes
- Gaussian Process Networks
- Almost Optimal Intervention Sets for Causal Discovery
- ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
- Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
- Probabilistic Active Learning of Functions in Structural Causal Models