Sadly, there is often a disconnect between the questions, comparisons and contexts addressed in research studies, and those that policymakers must consider. We should encourage this sort of thinking - and make it more accessible by highlighting these three elements in any analysis that seeks to estimate a policy-relevant causal effect. But by focusing on the question, comparison and context, non-expert decision-makers could reasonably assess the relevance of this study to their policy decision. In terms of context, different results were seen in the South than in other regions - and results in the United States might not necessarily be generalizable to other countries. In addition, the specific comparisons made in this study would only be directly applicable to an ‘all-or-nothing’ closure decision, as few counties adopted an approach of keeping elementary schools open but middle and high schools closed. This study relates to county-level decisions on opening or closing in-person learning - not, for example, decisions at the state or national level. 1 to decide whether to restrict in-person learning in the face of a new pandemic wave. Robust designs allow for reasonable estimation of what would have happened in the communities with schools that re-opened, had they actually stayed closed.įor example, consider a county school board evaluating the results of the study by Ertem et al. 1), and/or well-selected comparison groups (as in Fukumoto et al. Appropriate causal inference therefore requires strong study designs such as randomization, longitudinal evaluation of communities with schools that did and did not reopen (as in Ertem et al. Thus, we are forced to use data from different groups - or in this case, different schools - to estimate what would be seen in the same group under the intervention state versus the comparator state in other words, a ‘causal contrast’. This comparison immediately raises the ‘fundamental problem of causal inference’ 3 - that, for any given school at any point in time, we can only observe outcomes under one state (for example, school reopening), whereas the other state is unobserved, or ‘counterfactual’. In one state, a well-defined group experiences the intervention of interest (for example, school reopening) in the other, that same group experiences a comparator condition (such as continued virtual learning). For policy decisions, we are almost always interested in a causal question - that is, one that compares outcomes (for example, case rates) under two different possible states of the world.
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