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The Synthetic Control method has pioneered a class of powerful data-driventechniques to estimate the counterfactual reality of a unit from donor units.At its core, the technique involves a linear model fitted on thepre-intervention period that combines donor outcomes to yield thecounterfactual. However, linearly combining spatial information at each timeinstance using time-agnostic weights fails to capture important inter-unit andintra-unit temporal contexts and complex nonlinear dynamics of real data. Weinstead propose an approach to use local spatiotemporal information before theonset of the intervention as a promising way to estimate the counterfactualsequence. To this end, we suggest a Transformer model that leverages particularpositional embeddings, a modified decoder attention mask, and a novelpre-training task to perform spatiotemporal sequence-to-sequence modeling. Ourexperiments on synthetic data demonstrate the efficacy of our method in thetypical small donor pool setting and its robustness against noise. We alsogenerate actionable healthcare insights at the population and patient levels bysimulating a state-wide public health policy to evaluate its effectiveness, anin silico trial for asthma medications to support randomized controlled trials,and a medical intervention for patients with Friedreich's ataxia to improveclinical decision-making and promote personalized therapy.

SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare