Quote From abababa:
This is interestingly close to the crux of how the general public see data vs how researchers see data.

In many glossy lifestyle magazines you'll see 'eating more (or less) red meat reduces cancer risk'. This often refers to a study in which there's a correlation. But as per the cliche collelation does not equal causation; the way a researcher views this data is skeptically (what if people who eat more red meat are also more predisposed to smoking?) .

It sounds a bit like you've been set up with a PhD tied to this intervention to evaluate it. From my experience, this might well mean you have a board of public sector stakeholders who won't go so far as saying they want you to show it did, but probably will be more critical of evidence that shows it didn't. You will thus learn early on navigating these waters as a researcher, which is not an easy task, but a valuable skill. If in the same situation, my first line would probably be to explain that, due to the cofactors, it's not possible to empirically show it 'worked';

Quote From Nead:
What software do you plan on using ?
Personally, I would compare the first 6 months of one year with the same 6 months of the next year. Rather than 1 year with a half a year.
THere are helpful guides online like Laerd, minitab has there own guide etc
But it all depends on the software you use.