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Poster
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Scott Sussex · Caroline Uhler · Andreas Krause

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @ None #None

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 a batch of experiments that each 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 of composite interventions. In this paper, we develop efficient algorithms for optimizing different objective functions quantifying the informativeness of a budget-constrained batch of experiments. By establishing novel submodularity properties of these objectives, we provide approximation guarantees for our algorithms. Our algorithms empirically perform superior to both random interventions and algorithms that only select single-variable interventions.

Author Information

Scott Sussex (Swiss Federal Institute of Technology)
Caroline Uhler (Massachusetts Institute of Technology)
Andreas Krause (ETH Zurich)

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