♪ [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.
very powerfully. I think in the case of two instruments
that come from a paper, mine with Bill Evans. Where if you,
you know, replace it
in a traditional two stage least squares, estimator with some kind of random Forest.
You get very precisely at estimated nonsense estimates and
You know, I think that's a, that's a big caution.
And I, you know, in view of those findings
in an example, I care about where the instruments are very simple
and I believe that they're valid,
you know, I would be skeptical of that. So
non-linearity and Ivy don't mix very comfortably. Now I said,
you know in some sense that's already a more complicated. Well, it's Ivy.
Yeah,
but then we work on that and friend out.
I sat in tow vehicle actually guy a lot of these papers Cross by my desk and it,
but the motivation is is not clear at a fact, really lacking.
And they're not, they're not, they called type semi-parametric foundational papers.
So that that's a big problem
and kind of related problem is that we have this tradition in econometrics
being very focused on these formulas and tonic results kind of weird.
We have just have a lot of papers that where you people, propose
a method and then establish the asymptotic properties
in in a very kind of standardized way that bad.
Well, I think it's sort of close the door for a lot of work.
That doesn't fit it into that. We're in the machine learning literature.
A lot of things are more algorithmic people.
Had algorithms for coming up with predictions.
The turn out to actually work much better than say, nonparametric kernel regression
for a long-ass time. We're doing all the nonparametric syndecan, metrics.
We do it using kernel regression and I was great for proving theorems.
You could get confidence, 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. I just way way better,
but they didn't have to the proper. That's not my beef with machine learning theory.
As we know my name, I'm saying there for the prediction part.
It does much better. Yeah, that'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.
You know, when when Brian was doing his regression trees that just didn't fit in
and I think he would have had a very hard time.
Polishing these things. And it could have had six journals.
I, so I think we're we limited ourselves too much and we
that left us close things off
for a lot of these machine learning methods, that actually very useful.
Hmm. I mean, I think they're in general,
that literature the computer.
Scientists have brought a huge number of these algorithms.
The have proposed a huge number of these algorithms that actually very useful
at that are
Affecting the way we're going to be doing empirical work,
but we've not fully internalize 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 quote, 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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