# Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning

@inproceedings{Sussex2021NearOptimalME, title={Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning}, author={Scott Sussex and Andreas Krause and Caroline Uhler}, booktitle={NeurIPS}, year={2021} }

Causal structure learning is a key problem in many domains. Causal structures can be learnt by performing experiments on the system of interest. We address the largely unexplored problem of designing experiments that simultaneously intervene on multiple variables. While potentially more informative than the commonly considered single-variable interventions, selecting such interventions is algorithmically much more challenging, due to the doubly-exponential combinatorial search space over sets…

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