[MUSIC] Randomized experiments are both powerful and simple.
All we do is take a bunch of units, and split them into a treatment group and a control
group, and compare what happens.
Often times that's just impossible.
We just can't do it.
So for example, here I've got a picture showing global temperatures from the year 1900 to
2000.
And the year 1900, the temperature is normalized to zero, so on the vertical axis, we see the
difference from the 1900 temperature, or the temperature anomaly.
So we see over the past hundred years, global temperatures have increased by about 0.75
degrees Celsius.
That's what the data says.
So why?
Why have temperatures increased?
Was it caused by humans or not?
What do I mean by “was it caused by humans?”
What I mean is that, in a counterfactual world where humans did not exist, would the global
temperature have been 0.75 degrees cooler in the year 2000?
If yes, then we can conclude that humans caused that increase in global warming.
But here's the problem: we can't do an experiment.
We can't randomly take a whole bunch of planets, assign them to have humans, take a whole bunch
of other planets, assign them not to have humans, wait a hundred years, and then compare
their global temperatures.
We just can't do that.
We've only got one planet.
So what do we do instead?
Well, climate scientists attempt to model the climate system to try to figure out all
of the different physical processes that affect the outcome variable: global temperatures.
One of these variables is human-produced carbon dioxide emissions.
That variable might affect global temperatures.
So we include that in our model.
That's our treatment variable.
So, once we've got our model of all the different things that could affect global temperatures,
we then, in Step 2, fit it to the data.
We've got all our past temperature data, and we try to get our model to explain what we've
seen as good as possible in some statistical sense.
Then, once we've fitted our model, in Step 3, we can use our model to compute a counterfactual
outcome to see what would happen if humans hadn't existed.
Well, how do we do that?
It's actually pretty simple.
You just take that variable of human-produced global carbon dioxide emissions and set it
to zero for each year, and then see what the model predicts global temperatures would have
been.
Well, in this next picture, this is exactly what we've got right here.
This is the same kind of chart I just showed: global temperatures from 1900 to 2000.
And the temperature of the year 1900 is normalized to zero, and so the vertical axis is plotting
the anomaly from the 1900 temperature.
Now in pink, we see “models using both natural and anthropogenic forcings.”
That means including humans, so we have humans in the model.
So that pink line shows us exactly what we saw on the previous graph.
And this black line is the data.
That's the truth.
That's what actually happened in historical global temperatures.
Now let's compute our counterfactual outcome.
Let’s take our model and eliminate humans.
That's what the blue line shows: “models using only natural forcings” (i.e. not using
humans).
If we do that, we see the global temperatures are flat, and there's a big difference between
the model with humans and the model without humans, of about 0.75 degrees celsius.
That is the treatment effect.
That is what we predict the causal effect of humans to be on global warming.
So that's it.
We're done.
We’ve got our causal conclusion.
Well, where could things go wrong?
With this approach to learning about causality, it's all about the model.
You’ve really got to make sure you know how outcomes are produced.
What was amazing about randomized experiments is that we didn't need to know very much about
what causes outcomes.
How exactly are outcomes produced?
We just didn't need to know that.
We did our randomization to our treatment and control group, and we compared outcomes.
And that just tells us what the treatment effect is.
We don't need to know how other variables might affect outcomes.
It didn't matter.
Here, however, we do.
Here, our solution to the unobserved confounder problem is to try to observe all of the confounders
and include them in our model.
And once we've done that, we're done and we can draw conclusions like we just did.
So everything is about making sure we've got the right model.
So to do that you've got to talk to all your colleagues, really everybody who's working
in this area, and make sure you've included everything that could possibly matter.
Because that's what this and your conclusions hinge on: how good a model you have, and that's
it.
[MUSIC].