  We present Causal Amortized Active Structure Learning (CAASL), an active
intervention design policy that can select interventions that are adaptive,
real-time and that does not require access to the likelihood. This policy, an
amortized network based on the transformer, is trained with reinforcement
learning on a simulator of the design environment, and a reward function that
measures how close the true causal graph is to a causal graph posterior
inferred from the gathered data. On synthetic data and a single-cell gene
expression simulator, we demonstrate empirically that the data acquired through
our policy results in a better estimate of the underlying causal graph than
alternative strategies. Our design policy successfully achieves amortized
intervention design on the distribution of the training environment while also
generalizing well to distribution shifts in test-time design environments.
Further, our policy also demonstrates excellent zero-shot generalization to
design environments with dimensionality higher than that during training, and
to intervention types that it has not been trained on.
