Traditional panel-data causal inference frameworks, such as difference-in-differences and synthetic control methods, rely on pre-intervention data to estimate counterfactual means. However, such data may be unavailable 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 are unavailable. These methods leverage auxiliary reference domains with related panel data to estimate causal effects in the target domain, thereby overcoming the limitations imposed by the absence of pre-intervention data. We demonstrate the efficacy of these methods by deriving bounds on the absolute bias that converge to zero under suitable conditions, as well as through simulations across a variety of panel-data settings. Our proposed methodology renders causal inference feasible in urgent and data-constrained environments where the assumptions of existing causal inference frameworks are not met. As an application of our methodology, we evaluate the effect of a community organization vaccination intervention in Chelsea, Massachusetts on COVID-19 vaccination rates.

翻译：传统的面板数据因果推断框架，如双重差分法和合成控制法，依赖预干预数据来估计反事实均值。然而，在现实场景中，当干预措施是针对突发事件（如公共卫生危机或流行病冲击）而实施时，此类数据可能无法获得。本文针对预干预数据不可得的情形，提出了两种用于面板数据因果推断的数据融合方法。这些方法利用具有相关面板数据的辅助参考域，来估计目标域的因果效应，从而克服了因缺乏预干预数据所带来的局限。我们通过推导在适当条件下收敛于零的绝对偏差界限，以及在多种面板数据设置下的模拟实验，证明了这些方法的有效性。我们所提出的方法使得因果推断在紧急且数据受限的环境下成为可能，这些环境不满足现有因果推断框架的假设条件。作为该方法的一个应用，我们评估了马萨诸塞州切尔西市一项社区组织疫苗接种干预措施对COVID-19疫苗接种率的影响。