Professor Lockdown Now Claims to Have Saved 3.1 Million Lives | American Institute for Economic Research - Phillip W. Magness:
June 9, 2020 - "The problem of causal inference presents one of the great challenges of empirical analysis. While it is relatively easy to find patterns in data that appear to move over time in response to overlapping events, it is much harder to show that those events specifically caused the data to move as expected. Think about how presidents often cite positive economic data such as GDP growth or the stock market as vindication of their own economic policies. Prior to early March 2020 this was a favorite tweeting topic of Donald Trump, although his predecessors almost all made similar claims....
"[C]onsider the ongoing question about the effectiveness of the COVID-19 lockdown policies employed in several U.S. states as well as other countries. A causal inference test of the lockdowns would require clear evidence of different outcomes between states that adopted shelter-in-place rules and states that did not. Given the complex multitude of confounding variables affecting COVID-19 transmission and mortality rates, isolating causality from the lockdown policies is no easy task.
"That brings us to a new report published in the journal Nature by the epidemiology team at Imperial College-London (ICL).... This is the same epidemiology research center whose agent-based simulation model convinced the American and British governments to switch to a lockdown strategy.... The paper [in Nature] and an accompanying press release from the university put numbers to this claim, asserting that the lockdowns saved an estimated 3.1 million lives in Europe.
"Although this headline-grabbing claim will likely be treated as a vindication of the lockdown approach by its political supporters, a closer look at the analysis suggests the Imperial College team reached this conclusion without offering a viable causal inference strategy. As they describe in the paper, 'By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted in our study period' [stress added - gd]....
"Put differently, the epidemiologists reached their estimates by taking the difference between observed deaths and their own agent-based simulation.... They then depict the difference as if it demonstrates the validity of their own simulation model, despite providing no evidence that their original simulation was correct....
"If that line of argument sounds familiar, it’s because Donald Trump beat the Imperial College team to the punch. Citing the now-infamous March 16th ICL report by Imperial’s Neil Ferguson, the American president now regularly claims vindication for his own support of the lockdowns on account of the difference between its 2 million-plus projected death toll and the actual count of just over 100,000 as of this writing. As Trump tweeted on May 26, 'For all of the political hacks out there, if I hadn’t done my job well, & early, we would have lost 1 1/2 to 2 Million People, as opposed to the 100,000 plus that looks like will be the number.'
"Whether used by Imperial College or Trump, this line of argument falters as social science because it assumes the validity of the very same forecast it purports to demonstrate. Rather than testing the causal inference that lockdowns reduced the COVID death rate, it takes [its] own forecasted death rate as a given and then purports to calculate the number of lives saved by simple subtraction from its own model....
"The new paper essentially acknowledges as much in noting that 'the counterfactual model without interventions is illustrative only and reflects our model assumptions.' Rather than investigating this seemingly-crucial assertion further, let alone subjecting it to empirical testing, the authors indulge in a handwaving exercise that simply declares: '[all scenarios broadly show the same trends]. Given this agreement in differing scenarios we believe our estimates for the counterfactual deaths averted to be plausible'....
"The result is not a valid exercise in social scientific analysis, nor is it even an empirically robust test of the ICL model’s performance. Like Trump’s tweeting about both the economy before March 2020 and his own claimed role in reducing COVID deaths after the lockdowns, it is an exercise in statistical astrology. Sadly, unlike Trump’s tweets, however, the ICL managed to convince Nature, a top journal in the profession, to publish these unfounded claims."
Read more: https://www.aier.org/article/professor-lockdown-now-claims-to-have-saved-3-1-million-lives/
June 9, 2020 - "The problem of causal inference presents one of the great challenges of empirical analysis. While it is relatively easy to find patterns in data that appear to move over time in response to overlapping events, it is much harder to show that those events specifically caused the data to move as expected. Think about how presidents often cite positive economic data such as GDP growth or the stock market as vindication of their own economic policies. Prior to early March 2020 this was a favorite tweeting topic of Donald Trump, although his predecessors almost all made similar claims....
"[C]onsider the ongoing question about the effectiveness of the COVID-19 lockdown policies employed in several U.S. states as well as other countries. A causal inference test of the lockdowns would require clear evidence of different outcomes between states that adopted shelter-in-place rules and states that did not. Given the complex multitude of confounding variables affecting COVID-19 transmission and mortality rates, isolating causality from the lockdown policies is no easy task.
"That brings us to a new report published in the journal Nature by the epidemiology team at Imperial College-London (ICL).... This is the same epidemiology research center whose agent-based simulation model convinced the American and British governments to switch to a lockdown strategy.... The paper [in Nature] and an accompanying press release from the university put numbers to this claim, asserting that the lockdowns saved an estimated 3.1 million lives in Europe.
"Although this headline-grabbing claim will likely be treated as a vindication of the lockdown approach by its political supporters, a closer look at the analysis suggests the Imperial College team reached this conclusion without offering a viable causal inference strategy. As they describe in the paper, 'By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted in our study period' [stress added - gd]....
"Put differently, the epidemiologists reached their estimates by taking the difference between observed deaths and their own agent-based simulation.... They then depict the difference as if it demonstrates the validity of their own simulation model, despite providing no evidence that their original simulation was correct....
"If that line of argument sounds familiar, it’s because Donald Trump beat the Imperial College team to the punch. Citing the now-infamous March 16th ICL report by Imperial’s Neil Ferguson, the American president now regularly claims vindication for his own support of the lockdowns on account of the difference between its 2 million-plus projected death toll and the actual count of just over 100,000 as of this writing. As Trump tweeted on May 26, 'For all of the political hacks out there, if I hadn’t done my job well, & early, we would have lost 1 1/2 to 2 Million People, as opposed to the 100,000 plus that looks like will be the number.'
"Whether used by Imperial College or Trump, this line of argument falters as social science because it assumes the validity of the very same forecast it purports to demonstrate. Rather than testing the causal inference that lockdowns reduced the COVID death rate, it takes [its] own forecasted death rate as a given and then purports to calculate the number of lives saved by simple subtraction from its own model....
"The new paper essentially acknowledges as much in noting that 'the counterfactual model without interventions is illustrative only and reflects our model assumptions.' Rather than investigating this seemingly-crucial assertion further, let alone subjecting it to empirical testing, the authors indulge in a handwaving exercise that simply declares: '[all scenarios broadly show the same trends]. Given this agreement in differing scenarios we believe our estimates for the counterfactual deaths averted to be plausible'....
"The result is not a valid exercise in social scientific analysis, nor is it even an empirically robust test of the ICL model’s performance. Like Trump’s tweeting about both the economy before March 2020 and his own claimed role in reducing COVID deaths after the lockdowns, it is an exercise in statistical astrology. Sadly, unlike Trump’s tweets, however, the ICL managed to convince Nature, a top journal in the profession, to publish these unfounded claims."
Read more: https://www.aier.org/article/professor-lockdown-now-claims-to-have-saved-3-1-million-lives/
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