تدريب Shadowing: 1 8 What is Causation - تعلم التحدث بالإنجليزية عبر الفيديو

Two events can behave in a connected way without necessarily being causally connected.

They might simply be correlated.

For example, it might seem that a rooster crowing at dawn causes the sun to rise.

Certainly, the rooster crowing consistently precedes the sunrise in a very systematic way.

But how can we be sure that the rooster does not actually cause the sun to rise?

Well, we can intervene in some way on the rooster, making it impossible for him to crow, and nonetheless the sun will rise.

So, consistent pre-occurrence is not sufficient to distinguish true causation from a mere appearance of causation.

Other times, there might be what is called a lurking variable

that can account for the apparent link between events that appear to be causally linked.

Take for example the amount of clothing that people wear and the amount of ice cream that they eat.

The more clothing they wear, the less ice cream they eat.

And the more ice cream they eat, the less clothing they wear.

Does ice cream cause people to wear less or does wearing more clothing cause people to eat less ice cream?

No, of course not.

We can see that there's a lurking variable, in this case temperature, that drives both outcomes.

As it gets hotter outside, people tend to want to wear less clothing and they also want to eat more ice cream.

So neither temporal correlation nor any other kind of correlation in outcomes is sufficient to establish true causation between events.

How can we establish the existence of a genuine causal relationship between two things that both vary in time?

The most popular models of causation nowadays are called interventionist or manipulationist models of causation,

defended in recent years by thinkers such as Judea Pearl and John Woodward.

Their models of causation are rooted in the intuition that

if some variable associated with event A causes something to change in another event, B, then one should be able to manipulate A in some way

and see corresponding changes in B that happen after changing A.

If A is modeled as causing B, then there should be an intervention on A that results in B changing its value.

These kinds of models of causation basically describe what scientists have done for centuries to determine causal relationships among variables.

Scientists try to control for all independent variables.

calls this screening off the other variables besides A that likely partially cause B, by holding their values constant.

Then we vary the single variable A in order to see the consequences or changes

expressed by some outcome or dependent variable B.

If B changes with an intervention on A, it's concluded that A causes B, at least in part.

For example, if you manipulate the thermostat, and the furnace goes on,

and it gets warmer, that's a causal relationship because turning on the thermostat makes the furnace turn on.

But causation is not always so simple, especially in the brain and mind, as we shall see.

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