- [Narrator] What's the difference
between econometrics and data science?
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- [Josh] I would say that
the principal difference
is the approach
to the problem of prediction.
Data scientists are often concerned
with curve fitting type
approaches to prediction.
So any model
that fits the data will do.
If it's past experience, we might
be interested in using that
to extrapolate to the future.
A lot of the data science
agenda is tied
to somebody's marketing problems.
You're trying to figure out
who will buy something,
who will take some action.
Econometrics in my view deals
with kind of a harder class
of problems.
Econometricians are more concerned
with causal relationships.
In other words,
if we manipulate something,
say, health insurance
or monetary policy,
what's the world going to look like
in response to that change.
We don't take it for granted that
the past is a good guide to that
because we understand that variation
and variable is associated
with lots of potential
confounding variables,
we would say other things
that are moving
that also perhaps affect outcome.
The simple observed relationship
there is often misleading
because there are factors
that are not well controlled,
and we have in mind
that there is a research design
that involves more
than curve fitting.
In fact, we're fairly indifferent
to curve fitting in economics.
I think we want to know,
for example, whether it matters
if you go to an expensive
private college --
does that change your life course
in the form of higher earnings?
That's not really
a curve fitting question,
that's a causal question.
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- [Narrator] Ready
to master econometrics?
Click here to embark
on an educational journey
with Josh Angrist
aka Master Joshway.
Or, if you'd like to watch more
from this interview series,
click here.
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