Arguments about causality.

What about causality?

Correlation in itself doesn't prove causality.

It's very much about this causality, consequences.

All we're playing with is a tempo causality.

Nothing I'm saying here implies causality. I don't know.

So, I'll both begin and end this segment with causality.

Causality is one of the most disputed subject in Philosophy.

That we could understand the causality of the mental illnesses.

And that has something to say about causality or not.

That will allow us to make stronger claims about causality.

Uh, so there's this problem of trying to infer causality from correlations.

Another final comment, and again, trying to get it, stronger claims of causality,

If we're dealing with causality, and I don't even know for sure. I just...

So whatever is responsible for this cancellation is extremely tightly coupled with tempo causality.

Random representative sampling, independent and dependent variables, double blind, single blind experiments, confounds and causality.

When you think about you know, creativity has a lot to do with causality too.

Some philosophers are even saying that causality does not really exist. It's just an illusion.

There were no manipulations of an independent variable, so it's impossible to make any claims about causality.

I, I'm gonna start drawing these path models in the next segment with, with arrows that typically depict causality.

The other final comment I'd like to end on is again, I want to stress this issue of causality.

The power of randomized experiments is they allow for the strongest arguments or strongest claims we can possibly make about causality.

And then the idea of confounds. And the most important concept to take away from this segment is the idea of causality.

This notion of causality is important in debugging because when you fix a defect we want to make sure to think first.

But why is it the cause? This is so called counterfactual definition of causality which is the most used in our context.

All I'm saying is the arguments that we can make about causality are weaker, in these latter types of studies, than they are in the former.

Whatever the reason for the observed positive cross country correlation between income and democracy, it should not be confused with causality. Being democratic does not seem important in securing economic success.

And here it's really easy to see that there is something wrong, but jumping to an incorrect conclusion about causality when you see a correlation is the most often made logical mistake.

So we might have evidence for full mediation in a statistical sense But if we're just dealing with cross sectional, correlational data it doesn't mean we can say anything, make any strong arguments about causality.

As the baby hears a word in her language, the auditory areas light up, and then subsequently areas surrounding it that we think are related to coherence, getting the brain coordinated with its different areas, and causality, one brain area causing another to activate.

If there were, not only would it look like things were moving backward in time but everything we know about causality, cause and effect, the past versus the future, this would all get jumbled together in a way that doesn't really make sense to us.

So, as we go from x to the mediator to y, If those events happen over time so that the mediator follows x and the outcome follows the mediator, then again, we're moving towards scenarios where we can make stronger claims or stronger arguments of that causality.