  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.
