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- [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.
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.
We see 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.
And it was sort of clear...
that there was going to be a lot of heterogeneity there.
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 often people are going to click on it.
And so there you --
- [Josh] Why do I need machine learning to discover that?
It seems like I 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 course 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.
It's a 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 context.
- [Guido] I think the social science applications
you're talking about,
once were...
I think there's not a huge amount of heterogeneity in the effects.
- [Josh] Well, there might be if you allow me
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 [ ], 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 there 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 anyway.
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 is that...
- 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 with the stuff.
It's useful you are saying you are unconvinced
by the existing applications 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?
- [Guido] Yes.
It doesn't have a policy angle or something.
- The 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.
- They have proof of graduate school...
[laughter]
- 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.
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- [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 one area is where
instead of looking for average causal effects
we're looking for individualized estimates,
predictions of causal 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
are 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.
Non-linearity and IV don't mix very comfortably.
- No, it sounds like that's already a more complicated...
- Well, it's IV.... - Yeah.
- ...but then we work on that.
[laughter]
- Fair enough.
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- [Guido] As an editor of econometrics 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's a big problem.
A related problem is that we have this tradition in econometrics
of being very focused on these formal asymptotic results.
We just have a lot of papers where people propose a method
and then they 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,
and we were using kernel regression,
and that 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,
that the theory is weak.
[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 Breiman was doing his regression trees --
they just didn't fit in.
I think he would have had a very hard time
publishing these things in econometrics 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 brought a huge number of these algorithms there...
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 the machine learning literature.
- On the one hand, I guess I very much take your point
that sort of the traditional econometrics framework
of sort of propose a method, prove a limit theorem
under some asymptotic story, story story, story story...
publisher 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 about the role of...
Do you think there is no value in the theory part of it?
Because I guess a question that I often have
to sort of seeing the output from a machine learning tool,
and actually a number of the methods
that you talked about
actually do have inferential results
developed for them,
something that I always wonder about,
a sort of uncertainty quantification and just...
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 what you think about that.
- I don't see this as sort of saying, well,
these results are not interesting,
but it's going to 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 doing something
and it works well by some of the criterion there
on this particular data set,
and we should put it out there,
and maybe someone will figure out a way
that you can later actually still do inference
under some condition,
and maybe those are not particularly realistic conditions,
then we kind of go further.
But I think we've been constraining things too much
where we said,
"This is the type of things that we need to do.
And in some sense,
that goes back to the way Josh and I
thought about things for the [local average treatment] effect.
That wasn't quite the way
people were thinking about these problems before.
There was a sense that some of the people said
the way you need to do these things is you first say,
what you're interested in in estimating
and then you do the best job you can in estimating that.
And what you guys are doing is you're doing it backwards.
You kind of say, "Here, I have an estimator,
and now I'm going to figure out what it's estimating,
and I suppose you're going to say why you think that's interesting
or maybe why it's not interesting, and 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
in thinking about how to look at problems
because I think we've missed some things
by not doing that.
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- [Josh] So you've heard our views, Isaiah,
and you've seen that we have some points of disagreement.
Why don't you referee this dispute for us?
[laughter]
- Oh, it's so nice of you to ask me a small question.
So I guess for one,
I very much agree with something that Guido said earlier of...
[laughter]
- So one thing where it seems
where the case for machine learning seems relatively clear
is in settings where 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 kernel...
I would have run a kernel regression
or I would have run a series regression,
or something along those lines.
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 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're thinking of maybe the propensity score
or something like that?
- Yeah, exactly, - Nuisance functions.
- Yeah, so things like propensity scores,
even objects of more direct
interest-like conditional average treatment effects,
which are the difference of two conditional expectation functions,
potentially things like that.
Of course, even there, the theory...
for inference of the theory for 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 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
where people are doing tons of great work.
And so I fully 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 has sometimes led to applications
that have made less sense
is when folks start with a method that they're very excited about
rather than a question.
So sort of starting with a question
where here's the object I'm interested in,
here is the parameter of interest --
let me 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 a machine learning estimator for that --
that seems very, very sensible.
Whereas, you know, if I regress quantity on price
and say that I used a machine learning method,
maybe I'm satisfied that that solves the [ ] problem
we're usually worried about there... maybe I'm not.
But again, that's something
where the way to address it seems relatively clear.
It's to find your object of interest
and think about--
- Just bring in the economics.
- Exactly.
- And think about the heterogeneity,
but harnessed the power of the machine learning methods
for some of the components.
- Precisely. Exactly.
So the question of interest
is the same as the question of interest has always been,
but we now have better methods for estimating some pieces of this.
The place that seems harder to forecast
is obviously, there's a huge amount going on
in the machine learning literature
and the limited ways of plugging it in
that I've referenced so far
are a limited piece of that.
So I think there are all sorts of other interesting questions
about where...
where does this interaction go? What else can we learn?
And that's something where I think there's a ton going on
which seems very promising,
and I have no idea what the answer is.
- No, I totally agree with that,
but that makes it very exciting.
And I think there's just a little work to be done there.
Alright. So I say, he agrees with me there.
[laughter]
- I didn't say that per se.
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- [Narrator] If you'd like to watch more Nobel Conversations,
click here.
Or if you'd like to learn more about econometrics,
check out Josh's Mastering Econometrics series.
If you'd like to learn more about Guido, Josh, and Isaiah,
check out the links in the description.
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