Adam Kucharski

Or there's a lot of these kind of historical treatments which are just so overwhelmingly effective.

that we're not going to run trials.

So almost this hierarchy over time, you can see it getting shifted because actually you do have these situations where other forms of evidence can get you either closer to what you need or just more feasibly an answer where it's just not ethical or practical to do an RCT.

Yeah, I think this is, as emerged, is a really valuable tool.

It's kind of interesting in the book, you're talking to economists like Josh Angrist, that a lot of these ideas emerged in epidemiology, but I think were really then taken up by economists, particularly as they wanted to add more credibility to a lot of these kind of policy questions.

And ultimately, it comes down to this issue that for a lot of problems, we can't necessarily intervene and randomise.

but there might be a situation that's done it to some extent for us so the classic example is the Vietnam draft where it was kind of random birthdays were drawn out of lottery and so there's been a lot of studies subsequently about the effect of serving in the military on different you know subsequent lifetime outcomes because broadly those people have been randomized you know it was for a different reason but you've got that element of randomization driving that and so again you know with um

some of the recent singles data and other studies, you might have a situation, for example, where, you know, there's been an intervention that's somewhat arbitrary in terms of time, like, you know, it's a cutoff on a birth date, for example.

And under certain assumptions, you could think, well, actually, there's no real reason for the person on this day and this day to be fundamentally different.

I mean, perhaps there might be, you know, effects of kind of cohorts if it's school years or this sort of thing.

But generally, this isn't the same as having people who are very, very different ages and very different characteristics.

It's just,

Nature, or in this case, just a policy intervention for a different reason, has given you that randomization, which allows you, or kind of pseudo-randomization, which allows you to then look at something about the effects of an intervention that you wouldn't as reliably if you were just kind of digging into the data of kind of yes, no, who's received a vaccine.

Yeah, I think this is an issue that I think a lot of people get drilled into in their training.

You know, just because there's a correlation between things doesn't mean that that thing causes this thing.

But it really struck me as I talked to people researching the book, how in practice in research, there's actually a bit more to it and how it's played out.

So first of all, if there's a correlation between things, it doesn't tell you much generally that's useful for intervention.

You know, if two things are correlated, it doesn't mean that changing that thing is going to have an effect on that thing because there might be something that's influencing both of them, you know.

If you have more ice cream sales, it will lead to more heat stroke cases.

It doesn't mean that changing ice cream sales is going to have that effect.