Causal Data Fusion for Panel Data without a Pre-Intervention Period

TL;DR

This paper introduces two novel data-fusion methods for causal inference in panel data settings lacking pre-intervention data, enabling analysis in urgent scenarios like public health crises.

Contribution

It proposes new methods that leverage auxiliary reference domains to estimate causal effects without pre-intervention data, expanding the applicability of causal inference techniques.

Findings

Bounds on bias converge to zero under certain conditions

Methods perform well in simulations across various settings

Application to COVID-19 vaccination data demonstrates practical utility

Abstract

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.