(1/2) I've seen this paper getting a lot of attention but this is not proper causal reasoning. Imagine you are studying a population in which everyone has a very serious disease, except one person. Then, of course, you find that the disease explains little variation in happiness.
(2/4) Would you then conclude that the "effects are too small to warrant policy change"? Surely not. The low "variance explained" is due to low variation in exposure, but the effect of an intervention could be huge. Thus, if screen time affects one's happiness substantially,
(3/4) but almost everyone in the population has the same exposure to screen time, then screen time will surely explain little variation in happiness, since there is low variation of the exposure to begin with. Not sure if that's the case for the paper at hand, but they are not..
(3/3) answering the correct policy question. Finally, one thing that the low variance explained does imply is that it potentially makes it harder to rule out confounders that would explain away the observed association, see: researchgate.net/publication/32…
...this last one should've been (4/4)*
