Causal Data Fusion for Panel Data without Pre-Intervention Period

Traditional panel data causal inference frameworks, such as difference-in-differences and synthetic control methods, rely on pre-intervention data to estimate counterfactuals. However, such data may not be available in real-world settings when interventions are implemented in response to sudden events, such as public health crises or epidemiological shocks. In this paper, we introduce two data fusion methods for causal inference from panel data in scenarios where pre-intervention data is unavailable. These methods leverage auxiliary reference domains with related panel data to estimate causal effects in the target domain, overcoming the limitations imposed by the absence of pre-intervention data. We show the efficacy of these methods by obtaining converging bounds on the bias as well as through a simulation study. Our proposed methodology renders causal inference feasible in urgent and data-constrained environments where the assumptions of the existing causal inference frameworks are not met. As an application of the proposed methodology, we study the causal effect of the community organization activity on the COVID-19 vaccination rate among the Hispanic sub-population in the city of Chelsea, Massachusetts.