Dalton Conley and Tim Johnson - PNAS March 30, 2021 118 (13) e2104155118

Figure: Social scientists investigating COVID-related topics are not the first to use a historical moment to advance general knowledge about human behavior. Other one-time events, from the U.S. Vietnam-era draft lotteries to Hurricane Katrina to the 2008 financial crisis, have spawned important trends in social science research. Image credit: Dave Cutler (artist).

Over the last few decades, social scientists have experienced the causal revolution, the replication crisis, and, now in just a matter of months, another epoch: the era of coronavirus disease 2019 (COVID-19) research. According to Google Scholar, roughly 3.55 million COVID-19–related articles have appeared to date. That amounts to about 9,726 articles per day, or, roughly, one article every 9 seconds. Many of these articles are in the social sciences—that is, concerned not directly with medical outcomes but rather with COVID-19’s impact on social, behavioral, and economic outcomes.

Thus far, most of this research has had a direct focus on managing COVID-19, yet a growing number of articles enlist the pandemic to study basic questions about financial investment, education, politics, learning, crime, and other aspects of social life. As COVID-19 research in the social sciences moves toward basic science, we anticipate that it will increasingly intersect with the recent scholarly trends in the social sciences: the “causal revolution,” which shifted social scientists toward research designs that could establish causal relations between study variables instead of mere correlations, and the “replication crisis,” which focused social scientists on ensuring the reproducibility of published findings.

Researchers who incorporate the pandemic into causal inference strategies will encounter questions about the generalizability of findings produced by rigorously studying a historically distinct event. And others may question the usefulness of conclusions that rest on analyses that do not appear to be replicable in the traditional sense of rerunning an experiment. Fortunately, past research using “natural experiments” offers answers to such questions.