Adam Kucharski

And I think Bradford Hill's work, he was extremely thoughtful in how he approached this because something like smoking

you can't really design it like a trial.

You can't get people to randomly take up smoking and see if they get cancer.

There's obviously ethical reasons about it.

There's also just timeline reasons.

If you look at the timescale of the intervention versus what happened, you might have to wait decades to have that clear signal.

And so he did a lot of pioneering work with others, linking together the various sort of non-random data sets you had available.

Because one of the criticisms, of course, is any data is, yes, smokers are more likely to get cancer, but maybe there's a genetic reason that makes them more likely to smoke and

and get this.

And he outlined the ways we can think about cause and effect.

And I think that's

a very useful set of concepts, even some of it's the obvious ones of the cause needs to come before the effect.

Or that, you know, if you have this strength of association, more cigarettes makes you more likely to get cancer.

Or if you see that across multiple countries, or if you can start to think about, you know, the biological plausibility, we see carcinogens in other kinds of situations as well.

None of those things on their own is conclusive, but you can start to build this evidence base.

And he made this really good point that

Any knowledge we have, even if it's very confident knowledge, is always subject to further refinement.

But we still have that knowledge at that point in time and we can seek further information.

There's been lots more studies of smoking since their early ones.

But also that's information that we have to do something with.