From this Washington Post article:

But . . . wait a second. The University of Chicago’s Energy Policy Institute . . . what exactly is that?

Let’s do a google, then we get to the relevant page.

I’m concerned because this is the group that did this report, which featured this memorable graph:

See this article (“Evidence on the deleterious impact of sustained use of polynomial regression on causal inference”) for further discussion of statistical problem with this sort of analysis.

Anyway, the short story is that I’m skeptical about these numbers.

Does this matter? I dunno. I’m pretty sure that air pollution is bad for you, and I expect it does reduce life expectancy, so maybe you could say: So what if these particular numbers are off?

On the other hand, if all we want to say is that air pollution is bad for you, we can say that. The contribution of that Washington Post article and associated publicity is, first, the particular numbers being reported and, second, the claim about the magnitude of the effects.

So if I don’t trust the stats, why should I trust a claim like this?

I just don’t know what to think. Maybe the press should be careful about reporting these numbers uncritically. Or maybe it doesn’t matter because it’s a crisis so it’s better to have falsely-precise numbers than none at all. I’m concerned that the estimates being reported are overestimates because of the statistical significance filter.