How Will Machine Learning Impact Economics?

♪ [music] ♪

- [narrator] Welcome
to Nobel Conversations.

In this episode, Josh Angrist
and Guido Imbens

sit down with Isaiah Andrews
to discuss and disagree

over the role of machine learning
in applied econometrics.

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

where you guys largely agree,

but I'd like to turn to one

where maybe you have
some differences of opinion.

So I'd love to hear
some of your thoughts

about machine learning

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

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

so that there's
no published paper there.

There was an experiment
that was done

on some search algorithm.

And the question was...

it was about ranking things
and changing the ranking.

That was sort of clear...

that was going to be
a lot of heterogeneity there.

Mmm,

You know, if you look for say,

a picture of Britney Spears

that it doesn't really matter
where you rank it

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

whether you put it
in the first or second

or third position of the ranking.

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

if you put your book
first or your book tenth,

that's going to make
a big difference

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

And so there you go --

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

It seems like could
I can discover it simply?

- [Guido] So in general--

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

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

of the items

that you want to understand
what drives the heterogeneity

in the effect of--

- But you're just predicting

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

- [inaudible] it's causal effect,

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

Think about...

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

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

in whether the treatment
works for some groups or not.

And you have a lot of individual
characteristics,

and you want
to systematically search.

- Yeah. I'm skeptical about that --

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

that I should care about,

and that machine learning
can discover it

in some way that's useful.

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

going to, say, a charter school,

a publicly funded private school,

effectively, you know,
that's free to structure

its own curriculum
for context there.

Some types of charter schools

generate spectacular
achievement gains,

and in the data set
that produces that result,

I have a lot of covariance.

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

the education of the parents,

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

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

I have a very high dimensional space.

I'm definitely interested
in sort of coarse features

of that treatment effect,

like whether it's better for people

who come from
lower income families.

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

for the very high dimensional
version of that,

where I discovered
that for non-white children

who have high family incomes

but baseline scores
in the third quartile

and only went to public school
in the third grade

but not the sixth grade.

So that's what that high
dimensional analysis produces.

This very elaborate
conditional statement.

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

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

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

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

And I know also
that there's some alternative model

that fits almost as well,

that flips everything,

Because machine learning
doesn't tell me

that this is really
the predictor that matters.

It just tells me that
this is a good predictor.

And so, I think
there is something different

about the social science contest.

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

you're talking about,

once were...

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

- [Josh] There might be

if you allow me
to to fill that space.

- No... not even then.

I think for a lot
of those interventions,

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

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

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

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

and good for other people,

and that is kind
of very small pockets

where they're bad there.

But it may be some variation
in the magnitude,

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

I agree that in those cases,

they probably wouldn't be
very actionable anyone.

But I think there's a lot
of other settings

where there is
much more heterogeneity.

- Well, I'm open
to that possibility,

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

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

whether you need
to worry about the...

- Well, I need to see that paper.

- So the sense I'm getting...

- We still disagree on something.
- Yes.

[laughter]

- We haven't converged
on everything.

- I'm getting that sense.

[laughter]

- Actually, we've diverged on this

because this wasn't around
to argue about.

[laughter]

- Is it getting a little warm here?

- Warmed up. Warmed up is good.

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

that you're confident
that there is no way

that there is an application
where the stuff.

It's useful you are saying

you are unconvinced by
the existing application to date.

Fair enough.

- I'm very confident.

[laughter]

- In this case.

- I think Josh does have a point

that even in the prediction cases

where a lot of the machine learning
methods really shine

is where there's just a lot
of heterogeneity.

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

It doesn't have
a policy angle or something.

- They kind of recognizing
handwritten digits and stuff.

It does much better there

than building
some complicated model.

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

we actually know a huge amount
about the relationship

between its variables.

A lot of the relationships
are strictly monotone.

Education is going to increase
people's earnings,

irrespective of the demographic,

irrespective of the level
of education you already have.

- Until they get to a Ph.D.

- Yeah, there is a graduate school...

[laughter]

but go over a reasonable range.

It's not going
to go down very much.

In a lot of the settings

where these machine learning
methods shine,

there's a lot of [ ]

kind of multimodality
in these relationships,

and they're going to be
very powerful.

But I still stand by that.

These methods just have
a huge amount to offer

for economists,

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

- [Isaiah] Feels like
there's something interesting

to be said about
machine learning here.

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

maybe some examples
of the sorts of examples

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

- So on areas where

instead of looking
for average cause or effects

we're looking for
individualized estimates,

predictions of cause or effects

and the machine learning algorithms
have been very effective,

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

And theoretically they work great,

and there's some arguments

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

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

Random causal forest-type things

that Stefan Wager and Susan Athey
have been working on

have used very widely.

They've been very effective
in these settings

to actually get causal effects
that vary be [ ].

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

But in many cases,

these algorithms are very effective
as searching over big spaces

and finding the functions that fit very well

in ways that we couldn't
really do beforehand.

- I don't know of an example

where machine learning
has generated insights

about a causal effect
that I'm interested in.

And I do know of examples

where it's potentially
very misleading.

So I've done some work
with Brigham Frandsen,

using, for example, random forest
to model covariate effects

in an instrumental
variables problem

Where you need you need
to condition on covariance.

And you don't particularly
have strong feelings

about the functional form for that,

so maybe you should curve...

be open to flexible curve fitting,

and that leads you down a path

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

and that's very dangerous with IV

because any sort
of excluded non-linearity

potentially generates
a spurious causal effect

and Brigham and I
showed that very powerfully.

I think in the case
of two instruments

that come from a paper of mine
with Bill Evans,

where if you replace it

a traditional two stage
[ ] squares estimator

with some kind of random forest,

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

I think that's a big caution.

In view of those findings
in an example I care about

where the instruments
are very simple

and I believe that they're valid,

I would be skeptical of that.

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

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

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

- ...and we work on that.

[laughter]

- Fair enough.

- As Editor of Econometric [guy],

a lot of these papers
cross by my desk,

but the motivation is not clear

and, in fact, really lacking.

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

So that that's a big problem.

A related problem is that we have
this tradition in econometrics

of being very focused
on these formal [ ] results.

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

and then establish
the asymptotic properties

in a very kind of standardized way.

- Is that bad?

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

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

where in the machine
learning literature,

a lot of things
are more algorithmic.

People had algorithms
for coming up with predictions

that turn out
to actually work much better

than, say, nonparametric
kernel regression

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

we were using kernel regression,

and it was great for proving theorems.

You could get [ ] intervals

and consistency,
and asymptotic normality,

and it was all great,

But it wasn't very useful.

And the things they did
in machine learning

are just way, way better.

But they didn't have the problem--

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

[laughter]

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

it does much better.

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

- But it did so in a way

that would not have made
those papers

initially easy to get into,
the econometrics journals,

because it wasn't proving
the type of things.

When Brigham was doing
his regression trees

that just didn't fit in.

I think he would have had
a very hard time

publishing these things
in econometric journals.

I think we've limited
ourselves too much

that left us close things off

for a lot of these
machine learning methods

that are actually very useful.

I mean, I think, in general,

that literature,
the computer scientist,

have proposed a huge number
of these algorithms

that actually are very useful.

and that are affecting

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

But we've not fully internalized that

because we're still very focused

on getting point estimates
and getting standard errors

and getting P values

in a way that we need to move beyond

to fully harness the force,

the benefits
from machine learning literature.

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

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

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

story story publish a
paper is constraining.

And that in some sense by thinking, more,

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

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

which seem to work quite
well for a number of problems

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

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

The goal of fear.

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

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

that output from a machine learning tool

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

Actually do have inferential
results, develop for them,

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

you know, I I have my prior,

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

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

things are.

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

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

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

we do these results
are not not interesting

but it's gonna be a lot of cases

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

to get there and

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

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

The Criterion that on this
this particular data set

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

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

on the some condition.

So and maybe those are not
particularly realistic conditions,

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

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

that we need to do. And I had some sense

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

thought about things for the
local average treatment effect.

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

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

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

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

In estimating that

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

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

and now I'm going to figure out what what

what it says estimating then expose.

You're going to say why you
think that's interesting

or maybe why it's not interesting
and that's that's not okay.

You're not allowed to do that that way.

And I think we should just be a little
bit more flexible and thinking about the

how to look at at

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

So you've heard our views.
Isaiah, you've seen that, we have

some points of disagreement. Why
don't you referee this dispute for us?

Oh, I'm so so nice of you to ask me
a small question. So I guess for one.

I very much agree with something
that he do said earlier of.

So what?

Where it seems. Where the,

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

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

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

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

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

something along those lines.

Sort of,

it seems like

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

of applications machine learning
methods seem to do better for

Or, you know,

estimating conditional mean functions

or conditional probabilities or
various other nonparametric objects

than more traditional nonparametric
methods that were studied in econometrics

and statistics, especially
in high dimensional settings.

So you thinking of maybe the propensity
score or something like that?

So exactly, so nuisance functions. Yeah.

So things like propensity scores
things or I mean even objects

of more direct inference

interest, like conditional
average treatment effects, right?

Which of the difference of two
conditional, expectation functions,

potentially things like that.

Of course, even there,
right? We the the theory

for in France or the theory for
sort of how to how to interpret,

how to make large simple statements
about some of these things are

less well-developed depending on the
machine learning, estimator used.

And so, I think there's something
that is tricky is that we

can have these methods, which work a lot,

which seemed to work a lot
better for some purposes.

But which we need to be a bit
careful in how we plug them in or how

we interpret the resulting statements.

But of course, that's a very,
very active area right now. We're

People are doing tons of great work.
And so I exfoli expect and hope

to see much more going forward there.

So one issue with machine learning,
that always seems a danger is, or

that is sometimes a danger
and had some times led to

applications that have
made. Less sense, is

when folks start with a method that are

start with a method that they're very
excited about rather than a question,

right? So sort of starting with
a question where here's the

object I'm interested in here is
the parameter of Interest. Let me

You know,

think about how I would
identify that thing,

how I would recover that
thing, if I had a ton of data,

oh, here's a conditional
expectation function.

Let me plug in an estimator on
machine. Learning estimator for that.

That seems very very sensible.

Whereas, you know, if I
digress quantity on price

and say that I used a
machine learning method,

maybe I'm satisfied that that
solves the in dodging, 80 problem.

We're usually worried
about their maybe I'm not,

but again, that's something where the,

the way to address. It, seems
relatively clear, right?

It's the find your object of interest and

think about, is that just
bringing the economics?

Exactly.

And and can I think about it,
and they denied it, but harnessed

the power of the machine
learning methods for precisely

for some of the components precisely.
Exactly. So sort of, you know, the, the,

the question of interest is the same as
the question of interest is always been,

but we now better methods for estimating
some pieces of this, right? The

the place that seems harder to, uh,

harder to forecast is Right.

Obviously, there's a huge amount
going in going on in the machine.

Learning literature

and the great sort of The Limited ways

of plugging it in that I've referenced
so far are limited piece of that.

And so I think there are all sorts of
other interesting questions about where,

right sort of

where does this interaction
go? What else can we learn?

And that's something where,
you know, I think there's

a ton going on which seems very promising
and I have no idea what the answer is.

No, no. No, it's I so I totally
agree with that but it's no.

That's makes it very exciting.

And I think that's just a
little work to be done there.

All right. So I say agrees
with me there, say that person.

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