Abstract
Throughout the Covid-19 pandemic, people have been eager to learn what factors, and especially what public health policies, cause infection rates to wax and wane. But figuring out conclusively what causes what is difficult in complex systems with nonlinear dynamics, such as pandemics. We review some of the challenges that scientists have faced in answering quantitative causal questions during the Covid-19 pandemic, and suggest that these challenges are a reason to augment the moral dimension of conversations about causal inference. We take a lesson from Martha Nussbaum—who cautions us not to think we have just one question on our hands when we have at least two—and apply it to modeling for causal inference in the context of cost-benefit analysis.
Highlights
The Sturgis motorcycle rally was a real-world event, one that scientists had no control over. This means that researchers could not use the same methods they would use in scientific experiments, such as the randomized clinical trials (RCTs) that have been used to test Covid-19 vaccines
In an RCT, study participants are randomly assigned to two groups: the treatment group gets the experimental treatment, and the control group gets a placebo
When the randomization process in an RCT works as expected, Causal Inference, Moral Intuition, and Modeling in a Pandemic | 2 there will be no differences between these groups other than those resulting from chance
Summary
If someone skeptical asked, “How do you know vaccines cause reductions in Covid-19 cases, not something else?”, researchers would not have a good answer for them. Many questions that have come up during Covid-19 have been difficult to address with RCTs—policy-relevant questions such as: do mask mandates cause reductions in Covid-19 cases?—which has left researchers with observational data and modeling methods not unlike the ones used in the Sturgis rally study (Mitze et al 2020).
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