[MUSIC]
now we're ready to start talking about the different ways
we could actually learn about causality.
And we're gonna start with one of the oldest and best ways of doing
it, experiments.
Now remember the definition of a unit level causal effect, it's
the difference between your outcome when you're treated and your
outcome when you're not treated, holding all of your variables fixed.
So the easiest way to learn about unilevel causal effects is to
find two units who have every single variable exactly the same and
expect one of them is treated and one of them is not treated.
Then you just look at outcomes for the treated ones, the outcome
for the non-treated ones, you take the difference and you're done.
That's the idea behind the controlled experiments.
Now these are just the same things you see on TV infomercials where
they are testing paper towels and they say
here we have two paper towels, identical stains but this one is
our brand, and this one is that other brand, and look at how better
we are.
That's just a controlled experiment, so more serious examples, here
I have a picture of a plant growth chamber that they have in places
like Chicago botanical gardens where they used to do controlled
experiments on plants, so suppose you wanna know the effects of
higher CO2 levels on plant growth for example.
What you can do is you got two of this plant growth chambers, you
take two genetically equivalent plants, put one in one chamber,
one in the other.
Then you set all the variables the same in both chambers using this
control panel over here.
You set things like the humidity level, the day light level etc.
stuff like that except one variable you vary, CO2 level.
In one chamber you put it low, in the other chamber you put it high
Everything else is the same, then you wait for a while and you measure
plant growth.
And since the plants are exactly the same and all the variables
are the same, any differences in growth can solely be attributed
to differences in C02 levels.
So that's the controlled experiment.
Well with these kind of controlled experiments, you can in principal
just have two observations, one that's treated and one's that not
treated.
In practice you may wanna observe a little bit more just because
there is measurement error and how you measure the outcomes or the
treatment levels, CO2 for example but as far as causality is concerned
you only need one unit to be treated and one unit to not be treated.
Now in social science control experiments are basically impossible
and there are many reasons for this, the first
being that measuring variables is very difficult in social science.
So there are lots of things like ability and effort and intelligence
that are very, very difficult to measure and the idea behind a controlled
experiment is to get units that are exactly the same in all variables.
But if you can't measure every variable, then how do you know if
two units are the same?
Second, there are many, many, many variables that might matter in
the social science setting and even if you could measure every one
single of them, you probably not gonna be able to find two people
that are exactly identical, in every way except for you're gonna
treat one and you're gonna treat the other.
It's basically impossible.
And this is unlike in physical science like the plant growth chambers
where it's typically known that only few variables matter for these
things
So basically controlled experiments are out, so what's the solution?
The solution is to do randomized experiments which we will talk
about next time. [MUSIC]