Determine the true causal data-generating process and policy effects in epidemic outcomes

Determine the underlying causal data-generating process that produced observed epidemic outcomes during COVID-19 and identify whether government policy interventions causally affected these outcomes, including the direction and magnitude of any such effects.

Background

In critiquing multiverse analyses of government responses to COVID-19, the paper emphasizes that many estimated specifications may be far from causally valid. The author argues that counting the proportion of models indicating helpful or harmful associations is not informative when most specifications are biased and do not reflect the true underlying process.

Within this discussion, the author explicitly notes that the true causal data-generating process for the epidemic outcomes is unknown, as is whether policies have causal effects and, if so, their direction and magnitude. This underscores the need for hypothesis-driven causal modeling rather than model-agnostic multiverse aggregation.