is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
1/

The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/
The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
It is challenging to estimate the effect of interventions in the absence of a counterfactual. This difficulty is compounded by likely confounders, such as climate (not mentioned in the paper). Territories that implemented similar interventions might share comparable climate.
6/
This is a sophisticated piece of work, with both strengths and weaknesses. Though, to me, its primary value is the proposed methodological framework rather than its estimates of the efficacy of individual interventions, which efficacy remain debatable.
7/
There is room for scientific discussion about how solid the estimates presented in the paper may be. Though, accusing colleagues expressing reservations about the robustness of some of the findings of 'spreading disinformation' feels inadequate, to say the least.
8/
This paper has generated endless conflict. One intriguing feature is that all the spats are around 'educational settings'. Interestingly, the paper also claims that T&T and isolation of cases, among other measures, are completely ineffective, yet no one seems to care ... 🤔
9/

More from Science

This is a thread on statistics in science: 1/7 (via @LogicofScience)

Basic Statistics Part 1: The Law of Large Numbers https://t.co/wUH8eAAIak

#Science #Statistics


Basic Statistics Part 2: Correlation vs. Causation

https://t.co/Azhyl8pDsX (2/7)

Basic Statistics Part 3: The Dangers of Large Data Sets: A Tale of P values, Error Rates, and Bonferroni Corrections

https://t.co/LetN6aEBRM (3/7)

Basic statistics part 4: understanding P values

https://t.co/K8MMMgTCOf (4/7)

Basic Statistics Part 5: Means vs Medians, Is the “Average”

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