Natural Experiments Are Equal to Randomized Double-Blind Clinical Trials in Showing Causality

(p. B6) . . . randomized controlled trials are the gold standard in medicine. Using randomization (by, say, flipping a coin to assign patients to a new treatment or not) is the best way to determine whether treatments work.

Unfortunately, randomized trials take time — which is a problem when doctors need answers now. So doctors and public health officials have been turning to available real-world data on patient outcomes and trying to make sense of them.

. . .

“Large-scale randomized evaluations have been less common in economics, prioritizing the need for economists to identify often creative but sometimes narrow natural experiments to estimate the causal effects of treatments,” said Amitabh Chandra, an economist at the Harvard Business School and the Kennedy School of Government.

Ashish Jha, recently appointed the dean of the Brown University School of Public Health, said that while “natural experiments have causal interpretations, typical associational studies in medicine do not, which may make some medical researchers less comfortable interpreting the results.”

. . .   Most doctors can relate to recent comments by the Food and Drug Administration director Stephen Hahn in last week’s congressional pandemic hearing. “In a rapidly moving situation like we have now with Covid-19,” he said, decisions are made “based on the data that’s available to us at the time.”

For the full commentary, see:

Anupam B. Jena and Christopher M. Worsham. “THE UPSHOT; What Coronavirus Researchers Can Learn From Economists.” The New York Times (Thursday, July 2, 2020): B6.

(Note: ellipses added.)

(Note: the online version of the commentary has the date June 30, 2020, and has the same title as the print version.)

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