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♪ [music] ♪

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- [narrator] Welcome
to Nobel Conversations.

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In this episode, Josh Angrist
and Guido Imbens

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sit down with Isaiah Andrews
to discuss and disagree

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over the role of machine learning
in applied econometrics.

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- [Isaiah] So, of course,
there are a lot of topics

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where you guys largely agree,

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but I'd like to turn to one

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where maybe you have
some differences of opinion.

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So I'd love to hear
some of your thoughts

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about machine learning

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and the goal that it's playing
and is going to play in economics.

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- [Guido] I've looked at some data
like the proprietary

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so that there's
no published paper there.

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There was an experiment
that was done

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on some search algorithm.

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And the question was...

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it was about ranking things
and changing the ranking.

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That was sort of clear...

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that was going to be
a lot of heterogeneity there.

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Mmm,

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You know, if you look for say,

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a picture of Britney Spears

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that it doesn't really matter
where you rank it

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because you're going to figure out
what you're looking for,

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whether you put it
in the first or second

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or third position of the ranking.

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But if you're looking
for the best econometrics book,

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if you put your book
first or your book tenth,

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that's going to make
a big difference

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how much how often people
are going to click on it.

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And so there you go --

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- [Josh] Why do I need
machine learning to discover that?

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It seems like could
I can discover it simply?

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- [Guido] So in general--

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- [Josh] There were lots
of possible...

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- You what you want to think about
there being lots of characteristics

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of the items

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that you want to understand
what drives the heterogeneity

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in the effect of--

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- But you're just predicting

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In some sense, you're solving
a marketing problem.

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- [inaudible] it's causal effect,

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- It's causal, but it has
no scientific content.

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Think about...

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- No, but it's similar things
in medical settings.

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If you do an experiment, 
you may actually be very interested

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in whether the treatment
works for some groups or not.

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And you have a lot of individual
characteristics,

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and you want
to systematically search.

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- Yeah. I'm skeptical about that --

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that sort of idea that there's
this personal causal effect

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that I should care about,

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and that machine learning
can discover it

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in some way that's useful.

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So think about -- I've done
a lot of work on schools,

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going to, say, a charter school,

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a publicly funded private school,

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effectively, you know,
that's free to structure

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its own curriculum
for context there.

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Some types of charter schools

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generate spectacular
achievement gains,

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and in the data set
that produces that result,

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I have a lot of covariance.

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So I have baseline scores,
and I have family background,

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the education of the parents,

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the sex of the child, 
the race of the child.

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And, well, soon as I put
half a dozen of those together,

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I have a very high dimensional space.

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I'm definitely interested
in sort of coarse features

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of that treatment effect,

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like whether it's better for people

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who come from
lower income families.

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I have a hard time believing
that there's an application,

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for the very high dimensional
version of that,

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where I discovered
that for non-white children

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who have high family incomes

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but baseline scores
in the third quartile

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and only went to public school
in the third grade

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but not the sixth grade.

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So that's what that high
dimensional analysis produces.

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This very elaborate
conditional statement.

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There's two things that are wrong
with that in my view.

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First, I don't see it as...

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I just can't imagine
why it's actionable.

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I don't know why
you'd want to act on it.

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And I know also
that there's some alternative model

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that fits almost as well,

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that flips everything,

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Because machine learning
doesn't tell me

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that this is really
the predictor that matters.

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It just tells me that
this is a good predictor.

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And so, I think
there is something different

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about the social science contest.

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- [Guido] I think
the [socialized sign] applications

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you're talking about,

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once were...

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I think there's not a huge amount
of heterogeneity in the effects.

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- [Josh] There might be

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if you allow me
to to fill that space.

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- No... not even then.

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I think for a lot
of those interventions,

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you would expect that the effect
is the same sign for everybody.

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There may be small differences
in the magnitude, but it's not...

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For a lot of these education
defenses -- they're good for everybody.

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It's not that they're bad
for some people

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and good for other people,

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and that is kind
of very small pockets

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where they're bad there.

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But it may be some variation
in the magnitude,

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but you would need very, 
very big data sets to find those.

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I agree that in those cases,

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they probably wouldn't be
very actionable anyone.

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But I think there's a lot
of other settings

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where there is
much more heterogeneity.

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- Well, I'm open
to that possibility,

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and I think the example you gave
is essentially a marketing example.

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- No, those have implications for it
and that's the organization,

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whether you need
to worry about the...

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- Well, I need to see that paper.

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- So the sense I'm getting...

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- We still disagree on something.
- Yes.

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[laughter]

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- We haven't converged
on everything.

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- I'm getting that sense.

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[laughter]

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- Actually, we've diverged on this

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because this wasn't around
to argue about.

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[laughter]

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- Is it getting a little warm here?

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- Warmed up. Warmed up is good.

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The sense I'm getting is, Josh,
you're not saying

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that you're confident
that there is no way

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that there is an application
where the stuff.

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It's useful you are saying

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you are unconvinced by
the existing application to date.

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Fair enough.

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- I'm very confident.

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[laughter]

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- In this case.

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- I think Josh does have a point

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that even in the prediction cases

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where a lot of the machine learning
methods really shine

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is where there's just a lot
of heterogeneity.

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- You don't really care much
about the details there, right?

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It doesn't have
a policy angle or something.

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- They kind of recognizing
handwritten digits and stuff.

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It does much better there

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than building
some complicated model.

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But a lot of the social science,
a lot of the economic applications,

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we actually know a huge amount
about the relationship

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between its variables.

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A lot of the relationships
are strictly monotone.

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Education is going to increase
people's earnings,

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irrespective of the demographic,

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irrespective of the level
of education you already have.

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- Until they get to a Ph.D.

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- Yeah, there is a graduate school...

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[laughter]

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but go over a reasonable range.

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It's not going
to go down very much.

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In a lot of the settings

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where these machine learning
methods shine,

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there's a lot of [ ]

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kind of multimodality
in these relationships,

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and they're going to be
very powerful.

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But I still stand by that.

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These methods just have
a huge amount to offer

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for economists,

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and they're going to be
a big part of the future.

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- [Isaiah] Feels like
there's something interesting

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to be said about
machine learning here.

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So, Guido, I was wondering,
could you give some more...

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maybe some examples
of the sorts of examples

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you're thinking about
with applications [ ] at the moment?

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- So on areas where

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instead of looking
for average cause or effects

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we're looking for
individualized estimates,

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predictions of cause or effects

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and the machine learning algorithms
have been very effective,

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Traditionally, we would have done
these things using kernel methods.

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And theoretically they work great,

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and there's some arguments

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that, formally, 
you can't do any better.

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But in practice, 
they don't work very well.

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Random causal forest-type things

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that Stefan Wager and Susan Athey
have been working on

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have used very widely.

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They've been very effective
in these settings

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to actually get causal effects
that vary be [ ].

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I think this is still just the beginning
of these methods.

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But in many cases,

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these algorithms are very effective
as searching over big spaces

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and finding the functions that fit very well

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in ways that we couldn't
really do beforehand.

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- I don't know of an example

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where machine learning
has generated insights

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about a causal effect
that I'm interested in.

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And I do know of examples

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where it's potentially
very misleading.

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So I've done some work
with Brigham Frandsen,

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using, for example, random forest
to model covariate effects

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in an instrumental
variables problem

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Where you need you need
to condition on covariance.

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And you don't particularly
have strong feelings

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about the functional form for that,

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so maybe you should curve...

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be open to flexible curve fitting,

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and that leads you down a path

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where there's a lot
of nonlinearities in the model,

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and that's very dangerous with IV

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because any sort
of excluded non-linearity

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potentially generates
a spurious causal effect

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and Brigham and I
showed that very powerfully.

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I think in the case
of two instruments

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that come from a paper of mine
with Bill Evans,

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where if you replace it

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a traditional two stage 
[ ] squares estimator

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with some kind of random forest,

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you get very precisely
estimated [non-sense] estimates.

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I think that's a big caution.

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In view of those findings
in an example I care about

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where the instruments
are very simple

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and I believe that they're valid,

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I would be skeptical of that.

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So non-linearity and IV
don't mix very comfortably.

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No, it sounds like that's already
a more complicated...

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- Well, it's IV....
- Yeah.

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- ...and we work on that.

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[laughter]

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- Fair enough.

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- As Editor of Econometric [guy],

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a lot of these papers
cross by my desk,

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but the motivation is not clear

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and, in fact, really lacking.

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They're not... [we call] type
semi-parametric foundational papers.

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So that that's a big problem.

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A related problem is that we have
this tradition in econometrics

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of being very focused
on these formal [ ] results.

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We have just have a lot of papers
where people propose a method

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and then establish
the asymptotic properties

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in a very kind of standardized way.

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- Is that bad?

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- Well, I think it's sort
of closed the door

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for a lot of work
that doesn't fit it into that.

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where in the machine
learning literature,

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a lot of things
are more algorithmic.

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People had algorithms
for coming up with predictions

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that turn out
to actually work much better

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than, say, nonparametric
kernel regression

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For a long time, we were doing all
the nonparametrics in econometrics,

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we were using kernel regression,

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and it was great for proving theorems.

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You could get [ ] intervals

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and consistency, 
and asymptotic normality,

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and it was all great,

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But it wasn't very useful.

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And the things they did
in machine learning

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are just way, way better.

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But they didn't have the problem--

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- That's not my beef
with machine learning theory.

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[laughter]

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No, but I'm saying there,
for the prediction part,

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it does much better.

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- Yeah, it's a better
curve fitting to it.

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- But it did so in a way

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that would not have made
those papers

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initially easy to get into,
the econometrics journals,

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because it wasn't proving
the type of things.

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When Brigham was doing
his regression trees

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that just didn't fit in.

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I think he would have had
a very hard time

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publishing these things
in econometric journals.

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I think we've limited
ourselves too much

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that left us close things off

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for a lot of these
machine learning methods

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that are actually very useful.

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I mean, I think, in general,

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that literature, 
the computer scientist,

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have proposed a huge number
of these algorithms

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that actually are very useful.

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and that are affecting

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the way we're going
to be doing empirical work.

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But we've not fully internalized that

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because we're still very focused

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on getting point estimates
and getting standard errors

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and getting P values

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in a way that we need to move beyond

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to fully harness the force,

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the benefits
from machine learning literature.

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Hmm. On the one hand. I guess I very
much take your point that sort of the the

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Tional. Econometrics, framework
of sort of propose, a method,

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proved a limit theorem under some
asymptotic story, story story,

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story story publish a
paper is constraining.

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And that in some sense by thinking, more,

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broadly about what a methods paper could
look. Like we may write in some sense.

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Certainly the machine learning
literature has found a bunch of things,

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which seem to work quite
well for a number of problems

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and are now having substantial influence
in economics. I guess a question.

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I'm interested in is, how do you think?

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The goal of fear.

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Sort of, do you think there is? There's
no value in the theory part of it?

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Because I guess it's sort of a question
that I often have to sort of seeing

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that output from a machine learning tool

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that actually a number of the
methods that you talked about.

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Actually do have inferential
results, develop for them,

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something that I always wonder about a sort
of uncertainty quantification and just,

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you know, I I have my prior,

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I come into the world with my view.
I see the result of this thing.

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How should I update based on it? And
in some sense, if I'm in a world where

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things are.

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Normally distributed. I know
how to do it here. I don't.

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And so I'm interested to hear
had I think it sounds. So

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I don't see this as sort
of close it saying, well

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we do these results
are not not interesting

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but it's gonna be a lot of cases

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where it's going to be incredibly hard to
get those results and we may not be able

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to get there and

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we may need to do it in stages. Where
first someone says. Hey I have this

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interesting algorithm for for doing
something and it works well by some

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The Criterion that on this
this particular data set

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and I'm visit put it
out there and we should

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maybe someone will figure out a way that
you can later actually still do inference

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on the some condition.

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So and maybe those are not
particularly realistic conditions,

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then we kind of go further,
but I think we've been

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Too constraining things too much where we
said, you know, this is the type of things

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that we need to do. And I had some sense

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that goes back to kind of
the way they dress and I

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thought about things for the
local average treatment effect.

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That wasn't quite the way people
were thinking about these problems.

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Before they say they there was a sense
that some of the people said, you know,

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the way you need to do. These
things, is you first, say

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what you're interested in estimating
and then you do the best job you can.

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In estimating that

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and what you have you guys had doing is
doing it, you guys are doing it backwards.

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You're going to say
here. I have an estimator

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and now I'm going to figure out what what

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what it says estimating then expose.

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00:15:51,400 --> 00:15:53,900
You're going to say why you
think that's interesting

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00:15:53,900 --> 00:15:56,600
or maybe why it's not interesting
and that's that's not okay.

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00:15:56,600 --> 00:15:58,600
You're not allowed to do that that way.

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00:15:59,000 --> 00:16:04,100
And I think we should just be a little
bit more flexible and thinking about the

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how to look at at

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Problems because I think we've missed
some things by not by not doing that.

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00:16:13,000 --> 00:16:16,600
So you've heard our views.
Isaiah, you've seen that, we have

347
00:16:17,000 --> 00:16:20,400
some points of disagreement. Why
don't you referee this dispute for us?

348
00:16:22,500 --> 00:16:28,100
Oh, I'm so so nice of you to ask me
a small question. So I guess for one.

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00:16:28,200 --> 00:16:33,200
I very much agree with something
that he do said earlier of.

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00:16:36,000 --> 00:16:36,300
So what?

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Where it seems. Where the,

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the case for machine learning seems
relatively clear is in settings, where

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you know, we're interested in some version
of a nonparametric prediction problem.

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So I'm interested in estimating a conditional
expectation or conditional probability

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and in the past, maybe I
would have run a colonel,

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I would have run a kernel regression or
I would have run a series regression or

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something along those lines.

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Sort of,

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it seems like

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at this point we've a fairly good
sense that in a fairly wide range

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of applications machine learning
methods seem to do better for

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Or, you know,

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00:17:06,800 --> 00:17:08,800
estimating conditional mean functions

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or conditional probabilities or
various other nonparametric objects

365
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than more traditional nonparametric
methods that were studied in econometrics

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00:17:16,600 --> 00:17:19,100
and statistics, especially
in high dimensional settings.

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00:17:19,500 --> 00:17:23,100
So you thinking of maybe the propensity
score or something like that?

368
00:17:23,100 --> 00:17:25,300
So exactly, so nuisance functions. Yeah.

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00:17:25,300 --> 00:17:28,900
So things like propensity scores
things or I mean even objects

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00:17:28,900 --> 00:17:30,100
of more direct inference

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00:17:30,200 --> 00:17:32,400
interest, like conditional
average treatment effects, right?

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Which of the difference of two
conditional, expectation functions,

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potentially things like that.

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00:17:36,500 --> 00:17:40,400
Of course, even there,
right? We the the theory

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00:17:40,500 --> 00:17:43,700
for in France or the theory for
sort of how to how to interpret,

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00:17:43,700 --> 00:17:45,900
how to make large simple statements
about some of these things are

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00:17:46,000 --> 00:17:50,100
less well-developed depending on the
machine learning, estimator used.

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00:17:50,100 --> 00:17:53,800
And so, I think there's something
that is tricky is that we

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can have these methods, which work a lot,

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00:17:55,700 --> 00:17:58,000
which seemed to work a lot
better for some purposes.

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00:17:58,000 --> 00:18:01,600
But which we need to be a bit
careful in how we plug them in or how

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00:18:01,600 --> 00:18:03,300
we interpret the resulting statements.

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00:18:03,600 --> 00:18:06,200
But of course, that's a very,
very active area right now. We're

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00:18:06,400 --> 00:18:10,400
People are doing tons of great work.
And so I exfoli expect and hope

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00:18:10,400 --> 00:18:12,800
to see much more going forward there.

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00:18:13,000 --> 00:18:17,300
So one issue with machine learning,
that always seems a danger is, or

387
00:18:17,400 --> 00:18:20,300
that is sometimes a danger
and had some times led to

388
00:18:20,500 --> 00:18:22,600
applications that have
made. Less sense, is

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00:18:22,800 --> 00:18:25,100
when folks start with a method that are

390
00:18:25,300 --> 00:18:28,500
start with a method that they're very
excited about rather than a question,

391
00:18:28,900 --> 00:18:32,100
right? So sort of starting with
a question where here's the

392
00:18:32,500 --> 00:18:36,200
object I'm interested in here is
the parameter of Interest. Let me

393
00:18:36,700 --> 00:18:37,100
You know,

394
00:18:37,300 --> 00:18:39,500
think about how I would
identify that thing,

395
00:18:39,500 --> 00:18:41,800
how I would recover that
thing, if I had a ton of data,

396
00:18:41,900 --> 00:18:44,000
oh, here's a conditional
expectation function.

397
00:18:44,000 --> 00:18:47,100
Let me plug in an estimator on
machine. Learning estimator for that.

398
00:18:47,200 --> 00:18:48,800
That seems very very sensible.

399
00:18:49,000 --> 00:18:53,100
Whereas, you know, if I
digress quantity on price

400
00:18:53,700 --> 00:18:56,000
and say that I used a
machine learning method,

401
00:18:56,300 --> 00:18:58,900
maybe I'm satisfied that that
solves the in dodging, 80 problem.

402
00:18:58,900 --> 00:19:01,200
We're usually worried
about their maybe I'm not,

403
00:19:01,500 --> 00:19:03,200
but again, that's something where the,

404
00:19:03,400 --> 00:19:06,300
the way to address. It, seems
relatively clear, right?

405
00:19:06,500 --> 00:19:09,000
It's the find your object of interest and

406
00:19:09,200 --> 00:19:11,600
think about, is that just
bringing the economics?

407
00:19:11,700 --> 00:19:12,200
Exactly.

408
00:19:12,200 --> 00:19:15,400
And and can I think about it,
and they denied it, but harnessed

409
00:19:15,400 --> 00:19:18,300
the power of the machine
learning methods for precisely

410
00:19:18,500 --> 00:19:22,800
for some of the components precisely.
Exactly. So sort of, you know, the, the,

411
00:19:22,900 --> 00:19:25,600
the question of interest is the same as
the question of interest is always been,

412
00:19:25,600 --> 00:19:29,500
but we now better methods for estimating
some pieces of this, right? The

413
00:19:29,900 --> 00:19:31,600
the place that seems harder to, uh,

414
00:19:31,900 --> 00:19:33,400
harder to forecast is Right.

415
00:19:33,400 --> 00:19:36,300
Obviously, there's a huge amount
going in going on in the machine.

416
00:19:36,400 --> 00:19:37,400
Learning literature

417
00:19:37,500 --> 00:19:39,700
and the great sort of The Limited ways

418
00:19:39,700 --> 00:19:42,900
of plugging it in that I've referenced
so far are limited piece of that.

419
00:19:43,000 --> 00:19:46,100
And so I think there are all sorts of
other interesting questions about where,

420
00:19:46,300 --> 00:19:46,900
right sort of

421
00:19:47,100 --> 00:19:49,300
where does this interaction
go? What else can we learn?

422
00:19:49,300 --> 00:19:52,000
And that's something where,
you know, I think there's

423
00:19:52,200 --> 00:19:56,400
a ton going on which seems very promising
and I have no idea what the answer is.

424
00:19:57,000 --> 00:20:01,200
No, no. No, it's I so I totally
agree with that but it's no.

425
00:20:01,800 --> 00:20:03,500
That's makes it very exciting.

426
00:20:03,800 --> 00:20:06,100
And I think that's just a
little work to be done there.

427
00:20:06,600 --> 00:20:11,400
All right. So I say agrees
with me there, say that person.

428
00:20:14,500 --> 00:20:17,700
If you'd like to watch more
Nobel conversations, click here,

429
00:20:18,000 --> 00:20:20,400
or if you'd like to learn
more about econometrics,

430
00:20:20,500 --> 00:20:23,100
check out Josh's mastering
econometrics series.

431
00:20:23,600 --> 00:20:26,500
If you'd like to learn more
about he do Josh and Isaiah

432
00:20:26,700 --> 00:20:28,200
check out the links in the description.