  The Synthetic Control method has pioneered a class of powerful data-driven
techniques to estimate the counterfactual reality of a unit from donor units.
At its core, the technique involves a linear model fitted on the
pre-intervention period that combines donor outcomes to yield the
counterfactual. However, linearly combining spatial information at each time
instance using time-agnostic weights fails to capture important inter-unit and
intra-unit temporal contexts and complex nonlinear dynamics of real data. We
instead propose an approach to use local spatiotemporal information before the
onset of the intervention as a promising way to estimate the counterfactual
sequence. To this end, we suggest a Transformer model that leverages particular
positional embeddings, a modified decoder attention mask, and a novel
pre-training task to perform spatiotemporal sequence-to-sequence modeling. Our
experiments on synthetic data demonstrate the efficacy of our method in the
typical small donor pool setting and its robustness against noise. We also
generate actionable healthcare insights at the population and patient levels by
simulating a state-wide public health policy to evaluate its effectiveness, an
in silico trial for asthma medications to support randomized controlled trials,
and a medical intervention for patients with Friedreich's ataxia to improve
clinical decision-making and promote personalized therapy.
