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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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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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We see 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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And it was sort of clear

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that there was going to be
a lot of heterogeneity there.

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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 often people
are going to click on it.

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And so there you --

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

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It seems like -- because
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 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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- No, it's a 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 there'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, 
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 covariates.

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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 course 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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It's a 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 context.

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- [Guido] I think
the social science applications

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you're talking about

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are ones where,

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

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- [Josh] Well, there might be
if you allow me

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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
educational defenses --

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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 there 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 anyway.

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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

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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 is that --

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- We still disagree on something.
- Yes. [laughter]

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- We haven't converged
on everything.

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- I'm getting that sense.
[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
with the stuff.

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It's useful you are saying
you are unconvinced

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by the existing
applications to date.

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- Fair enough.
- 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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- [Guido] Yes.

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- It doesn't have
a policy angle or something.

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- The 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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- They don't have proof
of graduate school...

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[laughter]

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- 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 non-monotonicity

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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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♪ [music] ♪

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- [Isaiah] It 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

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with applications [inaudible]
at the moment?

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- So one area is where

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instead of looking
for average causal 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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are 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 by covariate.

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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
to condition on covariates.

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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, I think,

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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
least squares estimator

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with some kind of random forest,

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you get very precisely estimated
nonsense 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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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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- ...but then we work on that.

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[laughter]

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- Fair enough.

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♪ [music] ♪

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- [Guido] As an editor
of econometric guy,

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a lot of these papers
cross 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...

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[vehicle]-type semiparametric
foundational papers.

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So 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 asymptotic results.

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

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and then they 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 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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and we were using
kernel regression,

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and that was great
for proving theorems.

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You could get confidence 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,

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that the theory is weak.

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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 Breiman was doing
his regression trees --

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they 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 econometrics 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

00:12:29.622 --> 00:12:31.163
that are actually very useful.

00:12:31.163 --> 00:12:34.000
I mean, I think, in general,

00:12:34.900 --> 00:12:36.529
that literature, 
the computer scientist,

00:12:36.529 --> 00:12:40.013
have brought a huge number
of these algorithms there --

00:12:40.582 --> 00:12:42.632
have proposed a huge number
of these algorithms

00:12:42.632 --> 00:12:43.875
that actually are very useful.

00:12:43.887 --> 00:12:46.153
and that are affecting

00:12:46.153 --> 00:12:49.100
the way we're going
to be doing empirical work.

00:12:49.800 --> 00:12:52.105
But we've not fully
internalized that

00:12:52.105 --> 00:12:53.573
because we're still very focused

00:12:53.573 --> 00:12:57.500
on getting point estimates
and getting standard errors

00:12:58.600 --> 00:13:00.144
and getting P values

00:13:00.159 --> 00:13:03.209
in a way that we need
to move beyond

00:13:03.209 --> 00:13:06.090
to fully harness the force,

00:13:06.549 --> 00:13:08.351
the benefits

00:13:08.351 --> 00:13:10.700
from machine learning literature.

00:13:11.198 --> 00:13:13.548
- On the one hand, I guess I very
much take your point

00:13:13.548 --> 00:13:16.850
that sort of the traditional
econometrics framework

00:13:16.850 --> 00:13:19.821
of propose a method,
prove a limit theorem

00:13:19.821 --> 00:13:23.870
under some asymptotic story,
story, story, story, story...

00:13:24.424 --> 00:13:27.057
publisher paper is constraining,

00:13:27.218 --> 00:13:30.132
and that, in some sense,
by thinking more broadly

00:13:30.132 --> 00:13:31.699
about what a methods paper
could look like,

00:13:31.699 --> 00:13:33.316
we may in some sense.

00:13:33.316 --> 00:13:34.959
Certainly, the machine
learning literature

00:13:34.959 --> 00:13:37.189
has found a bunch of things
which seem to work quite well

00:13:37.189 --> 00:13:38.300
for a number of problems

00:13:38.300 --> 00:13:41.267
and are now having
substantial influence in economics.

00:13:41.267 --> 00:13:43.261
I guess a question
I'm interested in

00:13:43.261 --> 00:13:46.465
is how do you think
about the role of...

00:13:48.657 --> 00:13:51.200
Do you think there is no value
in the theory part of it?

00:13:51.600 --> 00:13:54.187
Because I guess a question
that I often have

00:13:54.187 --> 00:13:56.804
to seeing the output
from a machine learning tool,

00:13:56.804 --> 00:13:58.207
and actually a number
of the methods

00:13:58.207 --> 00:13:59.220
that you talked about

00:13:59.220 --> 00:14:00.679
actually do have
inferential results

00:14:00.679 --> 00:14:01.944
developed for them,

00:14:02.520 --> 00:14:03.963
something that
I always wonder about,

00:14:03.963 --> 00:14:06.659
a sort of uncertainty
quantification and just...

00:14:06.659 --> 00:14:08.000
I have my prior,

00:14:08.000 --> 00:14:11.000
I come into the world with my view,
I see the result of this thing.

00:14:11.000 --> 00:14:12.395
How should I update based on it?

00:14:12.395 --> 00:14:13.867
And in some sense, 
if I'm in a world

00:14:13.867 --> 00:14:15.914
where things
are normally distributed,

00:14:15.914 --> 00:14:17.280
I know how to do it --

00:14:17.280 --> 00:14:18.305
here I don't.

00:14:18.305 --> 00:14:21.028
And so I'm interested to hear
what you think about that.

00:14:21.500 --> 00:14:24.425
- I don't see this 
as sort of saying, well,

00:14:24.698 --> 00:14:26.556
these results are not interesting,

00:14:26.556 --> 00:14:27.968
but it's going to be a lot of cases

00:14:27.968 --> 00:14:30.153
where it's going to be incredibly
hard to get those results,

00:14:30.153 --> 00:14:32.489
and we may not
be able to get there,

00:14:32.489 --> 00:14:34.942
and we may need to do it in stages

00:14:34.942 --> 00:14:36.440
where first someone says,

00:14:36.440 --> 00:14:40.900
"Hey, I have
this interesting algorithm

00:14:40.900 --> 00:14:42.200
for doing something,

00:14:42.200 --> 00:14:47.769
and it works well
by some criterion there

00:14:47.769 --> 00:14:49.900
on this particular data set,

00:14:51.000 --> 00:14:52.602
and we should put it out there."

00:14:52.602 --> 00:14:55.410
and maybe someone
will figure out a way

00:14:55.410 --> 00:14:57.828
that you can later actually
still do inference

00:14:57.828 --> 00:14:59.463
under some condition,

00:14:59.463 --> 00:15:02.100
and maybe those are not
particularly realistic conditions,

00:15:02.100 --> 00:15:03.800
then we kind of go further.

00:15:03.800 --> 00:15:08.418
But I think we've been
constraining things too much

00:15:08.418 --> 00:15:09.519
where we said,

00:15:09.519 --> 00:15:13.185
"This is the type of things
that we need to do."

00:15:13.185 --> 00:15:14.502
And in some sense,

00:15:15.700 --> 00:15:18.200
that goes back
to the way Josh and I

00:15:19.700 --> 00:15:21.984
thought about things for the local
average treatment effect.

00:15:21.984 --> 00:15:23.137
That wasn't quite the way

00:15:23.137 --> 00:15:25.135
people were thinking
about these problems before.

00:15:25.805 --> 00:15:28.860
There was a sense
that some of the people said

00:15:29.500 --> 00:15:31.900
the way you need to do
these things is you first say

00:15:32.200 --> 00:15:34.250
what you're interested
in estimating,

00:15:34.250 --> 00:15:37.507
and then you do the best job
you can in estimating that.

00:15:38.100 --> 00:15:43.874
And what you guys are doing
is you're doing it backwards.

00:15:44.300 --> 00:15:46.700
You kind of say,
"Here, I have an estimator,

00:15:47.300 --> 00:15:50.642
and now I'm going to figure out
what it's estimating."

00:15:50.642 --> 00:15:53.900
And I suppose you're going to say
why you think that's interesting

00:15:53.900 --> 00:15:56.600
or maybe why it's not interesting,
and that's not okay.

00:15:56.600 --> 00:15:58.600
You're not allowed
to do that in that way.

00:15:59.000 --> 00:16:02.026
And I think we should
just be a little bit more flexible

00:16:02.026 --> 00:16:06.648
in thinking about
how to look at problems

00:16:06.648 --> 00:16:08.328
because I think
we've missed some things

00:16:08.328 --> 00:16:11.300
by not doing that.

00:16:11.300 --> 00:16:12.819
♪ [music] ♪

00:16:12.819 --> 00:16:14.753
- [Josh] So you've heard
our views, Isaiah,

00:16:14.753 --> 00:16:18.191
and you've seen that we have
some points of disagreement.

00:16:18.191 --> 00:16:20.400
Why don't you referee
this dispute for us?

00:16:20.950 --> 00:16:22.394
[laughter]

00:16:22.500 --> 00:16:24.999
- Oh, it's so nice of you
to ask me a small question.

00:16:24.999 --> 00:16:26.212
[laughter]

00:16:26.425 --> 00:16:27.993
So I guess, for one,

00:16:27.993 --> 00:16:33.200
I very much agree with something
that Guido said earlier of...

00:16:34.100 --> 00:16:35.710
[laughter]

00:16:35.920 --> 00:16:37.148
So one thing where it seems

00:16:37.148 --> 00:16:40.066
where the case for machine learning
seems relatively clear

00:16:40.066 --> 00:16:43.316
is in settings where
we're interested in some version

00:16:43.316 --> 00:16:45.100
of a nonparametric
prediction problem.

00:16:45.100 --> 00:16:46.392
So I'm interested in estimating

00:16:46.392 --> 00:16:49.700
a conditional expectation
or conditional probability,

00:16:50.000 --> 00:16:52.100
and in the past, maybe
I would have run a kernel...

00:16:52.100 --> 00:16:53.526
I would have run
a kernel regression

00:16:53.526 --> 00:16:55.184
or I would have run
a series regression,

00:16:55.184 --> 00:16:57.400
or something along those lines.

00:16:57.976 --> 00:17:00.350
It seems like, at this point, 
we've a fairly good sense

00:17:00.350 --> 00:17:03.102
that in a fairly wide range
of applications,

00:17:03.102 --> 00:17:05.671
machine learning methods
seem to do better

00:17:05.671 --> 00:17:08.610
for estimating conditional
mean functions,

00:17:08.610 --> 00:17:09.811
or conditional probabilities,

00:17:09.811 --> 00:17:12.000
or various other
nonparametric objects

00:17:12.400 --> 00:17:15.309
than more traditional
nonparametric methods

00:17:15.309 --> 00:17:17.292
that were studied
in econometrics and statistics,

00:17:17.292 --> 00:17:19.100
especially in
high-dimensional settings.

00:17:19.500 --> 00:17:21.849
- So you're thinking of maybe
the propensity score

00:17:21.849 --> 00:17:23.155
or something like that?

00:17:23.155 --> 00:17:25.063
- Yeah, exactly,
- Nuisance functions.

00:17:25.063 --> 00:17:27.100
- Yeah, so things
like propensity scores.

00:17:27.872 --> 00:17:29.965
Even objects of more direct

00:17:29.965 --> 00:17:32.400
interest-like conditional
average treatment effects,

00:17:32.400 --> 00:17:35.100
which are the difference of two
conditional expectation functions,

00:17:35.100 --> 00:17:36.625
potentially things like that.

00:17:36.625 --> 00:17:40.573
Of course, even there, 
the theory...

00:17:40.573 --> 00:17:43.620
for inference of the theory
for how to interpret,

00:17:43.620 --> 00:17:45.797
how to make large sample statements
about some of these things

00:17:45.797 --> 00:17:47.733
are less well-developed
depending on

00:17:47.733 --> 00:17:50.100
the machine learning
estimator used.

00:17:50.100 --> 00:17:52.983
And so I think
something that is tricky

00:17:52.983 --> 00:17:55.700
is that we can have these methods,
which work a lot,

00:17:55.700 --> 00:17:58.000
which seem to work
a lot better for some purposes

00:17:58.000 --> 00:18:01.229
but which we need to be a bit
careful in how we plug them in

00:18:01.229 --> 00:18:03.300
or how we interpret
the resulting statements.

00:18:03.600 --> 00:18:05.985
But, of course, that's a very,
very active area right now

00:18:05.985 --> 00:18:07.568
where people are doing
tons of great work.

00:18:07.568 --> 00:18:10.694
And so I fully expect
and hope to see

00:18:10.694 --> 00:18:12.800
much more going forward there.

00:18:13.000 --> 00:18:16.780
So one issue with machine learning
that always seems a danger is...

00:18:16.780 --> 00:18:18.517
or that is sometimes a danger

00:18:18.517 --> 00:18:20.938
and has sometimes
led to applications

00:18:20.938 --> 00:18:22.139
that have made less sense

00:18:22.139 --> 00:18:27.309
is when folks start with a method
that they're very excited about

00:18:27.309 --> 00:18:28.676
rather than a question.

00:18:28.900 --> 00:18:30.492
So sort of starting with a question

00:18:30.492 --> 00:18:33.782
where here's the object
I'm interested in,

00:18:33.782 --> 00:18:35.228
here is the parameter
of interest --

00:18:35.529 --> 00:18:39.500
let me think about how I would
identify that thing,

00:18:39.500 --> 00:18:41.824
how I would recover that thing
if I had a ton of data.

00:18:41.824 --> 00:18:44.000
Oh, here's a conditional
expectation function,

00:18:44.000 --> 00:18:47.065
let me plug in a machine
learning estimator for that --

00:18:47.065 --> 00:18:48.800
that seems very, very sensible.

00:18:49.000 --> 00:18:52.964
Whereas, you know, 
if I regress quantity on price

00:18:53.504 --> 00:18:56.000
and say that I used
a machine learning method,

00:18:56.300 --> 00:18:58.791
maybe I'm satisfied that 
that solves the endogeneity problem

00:18:58.791 --> 00:19:01.200
we're usually worried
about there... maybe I'm not.

00:19:01.500 --> 00:19:02.649
But, again, that's something

00:19:02.649 --> 00:19:06.300
where the way to address it
seems relatively clear.

00:19:06.500 --> 00:19:08.181
It's to find
your object of interest

00:19:08.181 --> 00:19:09.779
and think about --

00:19:09.779 --> 00:19:11.489
- Just bring in the economics.

00:19:11.489 --> 00:19:12.741
- Exactly.

00:19:12.741 --> 00:19:14.274
- And think about
the heterogeneity,

00:19:14.274 --> 00:19:17.067
but harness the power
of the machine learning methods

00:19:17.067 --> 00:19:20.148
for some of the components.

00:19:20.349 --> 00:19:21.388
- Precisely. Exactly.

00:19:21.388 --> 00:19:23.753
So the question of interest

00:19:23.753 --> 00:19:25.767
is the same as the question
of interest has always been,

00:19:25.767 --> 00:19:28.493
but we now have better methods
for estimating some pieces of this.

00:19:29.900 --> 00:19:32.704
The place that seems
harder to forecast

00:19:32.704 --> 00:19:35.816
is obviously there's
a huge amount going on

00:19:35.816 --> 00:19:37.500
in the machine learning literature,

00:19:37.500 --> 00:19:40.223
and the limited ways
of plugging it in

00:19:40.223 --> 00:19:41.388
that I've referenced so far

00:19:41.388 --> 00:19:43.090
are a limited piece of that.

00:19:43.090 --> 00:19:45.324
So I think there are all sorts
of other interesting questions

00:19:45.324 --> 00:19:46.520
about where...

00:19:47.100 --> 00:19:49.300
where does this interaction go? 
What else can we learn?

00:19:49.300 --> 00:19:52.932
And that's something where
I think there's a ton going on,

00:19:52.932 --> 00:19:54.414
which seems very promising,

00:19:54.414 --> 00:19:56.400
and I have no idea
what the answer is.

00:19:57.000 --> 00:20:00.297
- No, I totally agree with that,

00:20:00.297 --> 00:20:03.539
but that makes it very exciting.

00:20:03.539 --> 00:20:06.100
And I think there's just
a little work to be done there.

00:20:06.600 --> 00:20:08.720
Alright. So I say, 
he agrees with me there.

00:20:08.720 --> 00:20:10.174
[laughter]

00:20:10.174 --> 00:20:11.633
- I didn't say that per se.

00:20:12.926 --> 00:20:14.419
♪ [music] ♪

00:20:14.419 --> 00:20:16.833
- [Narrator] If you'd like to watch
more Nobel Conversations,

00:20:16.833 --> 00:20:18.012
click here.

00:20:18.012 --> 00:20:20.492
Or if you'd like to learn
more about econometrics,

00:20:20.500 --> 00:20:23.100
check out Josh's
Mastering Econometrics series.

00:20:23.600 --> 00:20:26.569
If you'd like to learn more
about Guido, Josh, and Isaiah,

00:20:26.569 --> 00:20:28.550
check out the links
in the description.

00:20:28.550 --> 00:20:30.535
♪ [music] ♪