As far as I can see they are inferring casualty (cautiously) because there are other regions that haven’t seen the same drastic uptake in family planning on that year as the region they worked in, and no other major player they know of had a major campaign in that area.
They also said they are engaging with researchers to see how much of the increase in uptake might be due to their work, and said that early results are promising. I struggle to understand what they mean by this and have asked above and hope for a reply.
I would agree that it is dangerous to make any strong claim of casualty from any before and after cross sectional research in such a complex scenario. Because of this nature of the survey I don’t think FEM can claim cause and effect here with high certainty.
But assuming the survey is sound (I had a look and it seems solid) then I would say at least there is a good chance that some proportion of the increase in uptake is likely because of FEMs work—but how much they could claim is very difficult to ascertain.
They might have other data they are using sad well which I can’t see here though, but will wait for a comment from FEM.
“intervention period indicated by shaded area”?
The shaded area is between the two data points, which means that the two data points are one before and one after the intervention period.
Right, my question is how can you infer causality from the data?
Thanks Paula, it’s a good question
As far as I can see they are inferring casualty (cautiously) because there are other regions that haven’t seen the same drastic uptake in family planning on that year as the region they worked in, and no other major player they know of had a major campaign in that area.
They also said they are engaging with researchers to see how much of the increase in uptake might be due to their work, and said that early results are promising. I struggle to understand what they mean by this and have asked above and hope for a reply.
I would agree that it is dangerous to make any strong claim of casualty from any before and after cross sectional research in such a complex scenario. Because of this nature of the survey I don’t think FEM can claim cause and effect here with high certainty.
But assuming the survey is sound (I had a look and it seems solid) then I would say at least there is a good chance that some proportion of the increase in uptake is likely because of FEMs work—but how much they could claim is very difficult to ascertain.
They might have other data they are using sad well which I can’t see here though, but will wait for a comment from FEM.