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0:00
Welcome back, everyone.
My name is Alessio Volpicella,
associate professor at
the University of Pavia.
That's going to be
the second lecture of
our journey to an
introduction to econometrics.
In particular, today,
we're going to start
looking into one of
the key challenges
in econometrics,
the distinction between
correlation and causation.
0:29
A short summary of what
we saw in Lecture 1.
We saw that basically
econometrics turns
economic questions into
measurable and
quantifiable answers
by merging economic
theories and models,
data, and statistics.
0:48
We're going to build
on that to introduce
the trickiest part
in econometrics.
As I said, distinguishing
causal effects from
pure correlations.
We're going to rely on
some real-world simple examples
to show why correlation
doesn't imply causation,
and we're going to start seeing
how econometric techniques help
uncover true causal
relationships.
1:16
First of all, a bit of language,
just to be sure we are
all on the same page.
By correlation, we mean
that two variables,
let's say X and Y move together,
and you can think of
any economic variable
you may be interested in.
That's different from causation,
which implies that
changing X makes Y change.
Now, there are mostly
three reasons why,
