I agree with the general concept. I would be a bit more careful in the conclusions, however: No visible correlation does not mean no causation—it is just a strong hint. In the specific example, the hint comes from a single parameter—the lack of significant correlation between internet & overweight when both exercise categories are added; together with the significant correlation of internet usage with the other two parameters.
With the proposed diagram, I get: p(Internet)=.141 p(not Internet)=.859 p(Overweight)=.209 p(not Overweight)=.791
p(Ex|Int & Ov)=.10 p(Ex|Int & no OV)=.62 p(Ex|no Int & Ov)=.27 p(Ex|no Int & no Ov)=.85
This model has 6 free parameters—the insignificant correlation between overweight and internet is the only constraint. It is true that other models have to be more complex to explain data, but we know that our world is not a small toy simulation—there are causal connections everywhere, the question is just “are they negligible or not?”.
Interesting article, thanks.
I agree with the general concept. I would be a bit more careful in the conclusions, however:
No visible correlation does not mean no causation—it is just a strong hint. In the specific example, the hint comes from a single parameter—the lack of significant correlation between internet & overweight when both exercise categories are added; together with the significant correlation of internet usage with the other two parameters.
With the proposed diagram, I get:
p(Internet)=.141
p(not Internet)=.859
p(Overweight)=.209
p(not Overweight)=.791
p(Ex|Int & Ov)=.10
p(Ex|Int & no OV)=.62
p(Ex|no Int & Ov)=.27
p(Ex|no Int & no Ov)=.85
This model has 6 free parameters—the insignificant correlation between overweight and internet is the only constraint. It is true that other models have to be more complex to explain data, but we know that our world is not a small toy simulation—there are causal connections everywhere, the question is just “are they negligible or not?”.