  We introduce a gradient-based approach for the problem of Bayesian optimal
experimental design to learn causal models in a batch setting -- a critical
component for causal discovery from finite data where interventions can be
costly or risky. Existing methods rely on greedy approximations to construct a
batch of experiments while using black-box methods to optimize over a single
target-state pair to intervene with. In this work, we completely dispose of the
black-box optimization techniques and greedy heuristics and instead propose a
conceptually simple end-to-end gradient-based optimization procedure to acquire
a set of optimal intervention target-state pairs. Such a procedure enables
parameterization of the design space to efficiently optimize over a batch of
multi-target-state interventions, a setting which has hitherto not been
explored due to its complexity. We demonstrate that our proposed method
outperforms baselines and existing acquisition strategies in both single-target
and multi-target settings across a number of synthetic datasets.
