
A plan to change how Harvard teaches economics - carlosgg
https://www.vox.com/the-highlight/2019/5/14/18520783/harvard-economics-chetty
======
Animats
This isn't an economics class. It's a public policy class. Here are the course
topics:[1]

\- Part I: Equality of Opportunity

\- Part II: Education

\- Part III: Racial Disparities

\- Part IV: Health

\- Part V: Criminal Justice

\- Part VI: Climate Change

\- Part VII: Tax Policy

\- Part VIII: Economic Development and Institutional Change

This really belongs in Harvard's "JFK School of Government", not economics.

Possible topics for a modern economics intro class:

\- Instability and equilibrium, or why markets oscillate.

\- From zero to one, the tendency to and effects of monopoly and near-
monopoly.

\- Externalities, their uses and discontents.

\- Debt vs. equity vs. what tax policy rewards

\- Scarce resources that don't map to money - attention and time.

\- Finance as a system decoupled from productive activity

[1] [https://opportunityinsights.org/wp-
content/uploads/2019/05/E...](https://opportunityinsights.org/wp-
content/uploads/2019/05/Econ1152_syllabus_spring19_forweb-1.pdf)

~~~
onlyrealcuzzo
Is there a single book that covers all that? That would be awesome.

~~~
garmaine
Schools like Harvard don’t teach from a book.

~~~
barry-cotter
Wrong side of the Atlantic. I didn’t have to buy a single textbook my entire
undergrad because we had libraries and lecture notes. Required textbooks are
far more a US thing than most other countries.

Harvard absolutely has courses taught from a book. I’m sure Mankiw requires
his textbook for Harvard’s intro econ course. He wrote it, he thinks it’s
good.

~~~
fmajid
And he's made $42 _million_ in royalties on the book. It's almost endearing
how economists claim they are somehow immune to incentives, until you pause to
consider they are primarily employed as apologists for the continuation of
rent-seeking policies that entrench the rich and mighty.

~~~
westurner
> _apologists for the continuation of rent-seeking policies that entrench the
> rich and mighty._

This.

"THE IMF CONFIRMS THAT 'TRICKLE-DOWN' ECONOMICS IS, INDEED, A JOKE"
[https://psmag.com/economics/trickle-down-economics-is-
indeed...](https://psmag.com/economics/trickle-down-economics-is-indeed-a-
joke)

> _INCREASING THE INCOME SHARE TO THE BOTTOM 20 PERCENT OF CITIZENS BY A MERE
> ONE PERCENT RESULTS IN A 0.38 PERCENTAGE POINT JUMP IN GDP GROWTH._

> _The IMF report, authored by five economists, presents a scathing rejection
> of the trickle-down approach, arguing that the monetary philosophy has been
> used as a justification for growing income inequality over the past several
> decades. "Income distribution matters for growth," they write.
> "Specifically, if the income share of the top 20 percent increases, then GDP
> growth actually declined over the medium term, suggesting that the benefits
> do not trickle down."_

"Causes and Consequences of Income Inequality: A Global Perspective" (2015)
[https://scholar.google.com/scholar?hl=en&as_sdt=0%2C43&q=%22...](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C43&q=%22Causes+and+Consequences+of+Income+Inequality%3A+A+Global+Perspective%22)

I'll add that we tend to overlook the level of government spending during
periods trickle-down economics and confound. Change in government spending
(somewhat unfortunately regardless of revenues) is a relevant factor.

Let's make this economy great again? How about you identify the decade(s)
you're referring to and I'll show you the tax revenue (on income and now
capital gains), federal debt per capital, and the growth in GDP.

------
viburnum
You can look through any mainstream micro textbook, graduate or undergrad, and
in 1,000 pages won’t see a single citation to support any model empirically.
Compare that to any decent physics textbook, which will link models to the
experiments that back them up. Economics for the real world won’t be simple
and pure like physics, it’ll be more like geology or biology, with a lot more
facts and a lot fewer theories of everything.

~~~
cjblomqvist
This is not true. I've been through an economics program and a lot of theories
have empirical data to support them. Of course it's much harder to prove there
is causation and not only correlation (and all that that entails), but that's
part of being a topic which to a large extent deal with social phenomena
(people).

Can't dig up anything now but a lot of phenomena in papers (though of course
far from all /majority) do or reference some sort of quantitative research).

~~~
soVeryTired
Meh. The standard of "support" is pretty low. I spent a lot of time looking
through the literature for empirical support for 1/ the Philips curve, and 2/
gravity models of trade. I looked for the first out of personal interest, and
for the second because economists often claim that gravity models are well-
supported.

Where empirical studies were actually available, they usually had large
numbers of parameters, they did not test on diverse datasets (e.g. Philips
curves across different economies), and they never considered an alternative
hypothesis or simple baseline.

~~~
fujimotos
Indeed. Even within a single economy, plotting a scatter plot for enough data
points (e.g. 25 years) makes Philips curves just broken.

It was a gong moment for me when I came across a short passage discussing its
foundedness.

* Freedman, Pisani and Purves Statistics, 4th edition (p153)

------
skanderbm
This initiative is very close in spirit to the CORE economics curriculum
([https://www.core-econ.org/](https://www.core-econ.org/)).

It focuses on observable social and economics consequences (failures of
equality, opportunity and sustainability in particular), when introducing new
economic concepts.

The goal is to avoid students being blinded by appealing, but simplistic,
economics models.

Having been seduced by many an economic theory before, I for one welcome this
perspective.

------
anbop
Cool. One thing I hated about my Econ minor was that so much of undergraduate
economics education assumes conditions which are never true. It’s like
studying aerospace engineering and only introducing air resistance in graduate
school.

~~~
333c
Good comparison. This is how I felt when I took ECON 101. Though I was
fascinated with the concepts, they felt so far from reality as to be
effectively useless.

~~~
notfromhere
That's what ECON 101 is, you need to get deeper into the curriculum. It's like
complaining intro to algebra didn't teach you differential equations

~~~
333c
The problem was that this stance was used to justify claims like "the free
market allocates resources in the most efficient way" and "all taxes incur a
deadweight loss" which are big statements to make based on such basic
coursework. If the class is going to make claims like these, it needs to start
with realistic assumptions.

------
sarosh
The course is EC1152 and titled “Big Data to Solve Economic and Social
Problems”. Lecture notes are at [https://opportunityinsights.org/wp-
content/uploads/2019/05/B...](https://opportunityinsights.org/wp-
content/uploads/2019/05/Big-Data-Course-Slides-in-PDF-Format-1.zip)

------
HillaryBriss
If I read this right, Chetty's new EC course tries not only to put the focus
on empirical data and its analysis, but also to disentangle ethical value
judgments from economics.

This is sorely needed. I've often thought that economists (on either side of
the political divide) love to make pompous pronouncements about what's
mathematically right and wrong and somehow translate that into what's
ethically right and wrong, and then say that their opponents are stupid and
evil.

~~~
kodz4
Chetty's own inequality/mobility data gets interpreted differently by the left
and the right. There is no free lunch here, when it comes to dealing with
people's vested interests (biases).

Policy/decision makers know anytime things get political the issue is never
about the data. If someone somewhere has to give up something, try doing it
just showing them your data and analysis.

You can run the experiment on HN. Try getting something in the architecture to
change based on data, and pitch it to the HN community. See what happens...

------
scottlocklin
Suggested parts, considering what Harvard econ has done for the human race:

IX: "How we looted the former Soviet Union, blew up their economy, got away
with it, and blamed it on the Russians" (team seminar by Andrei Shleifer and
Larry Summers)

X: "Selling your country to foreigners, indebting the masses for fun and
profit, then telling the fools the GDP got bigger" (Greg Mankiw)

XI: "Linear regression, with ideology" (everyone else)

~~~
bubblewrap
Can you explain how the US looted the former Soviet Union? Never heard that
one before.

~~~
sprafa
It’s considered among knowledgeable circles that the creation of the Russia we
know of today was largely the work of the “Chicago Boys” type group of
economists who were sent to liberalize the Russian economy through “shock
therapy”.

There are lots of well researched stories that show they did stupid badly
thought of things such as, famously, handing over stocks in major companies to
its workers, instantly making them shareholders.

That sounds fine in theory. But with poverty at their throat workers simply
sold their sto luck to the first person who asked. Often these were people
with “connections” or some money stashed away or criminally-funded
“entrepreneurs”, who with some barebones organisation and planning could
quickly scoop up billions of dollars worth of stock and take a controlling
position in major companies by buying them cents for a dollar.

Thus the oligarchs were born which quickly led to the kleptocratic Russia we
know of today.

Liberal minded and educated Russians realised western economists were
annihilating their country. One well known Russian independent magazine called
the newly rebuilt Russia a “neoliberal dystopia”. Which more and more I think
of as quite accurate.

~~~
bmmayer1
Which knowledgeable circles?

This seems demonstrably untrue: "Thus the oligarchs were born which quickly
led to the kleptocratic Russia we know of today"

Oligarchs have been a mainstay of Russian society since at least the time of
Peter the Great when one of his main policy positions as Tsar was reducing the
influence of the oligarchs (then called Boyars).

Plus, Russia under Soviet rule was unbelievably kleptocratic and corrupt.

~~~
crdoconnor
Most Russian oligarchs today trace their wealth back to the acquisition of
shares in 1991, often with the help of loans from banks run by friends.

Larry Summers, when he was "advising", didn't make a secret of the fact that
he didn't really care who "owned" most Russian assets provided it was in
private hands.

~~~
ionised
> Larry Summers, when he was "advising", didn't make a secret of the fact that
> he didn't really care who "owned" most Russian assets provided it was in
> private hands.

Ideology over practicality. Lovely.

------
asabjorn
I wonder how long it will take before the Ivys preference of social justice
over teaching core curricula will affect their market value.

~~~
SantalBlush
They probably don't see this false dichotomy of social justice vs. core
curricula, which would explain why they are fine with it.

------
carlosgg
The course is called Economics 1152. The video lectures and course slides are
available here:

[https://opportunityinsights.org/course/](https://opportunityinsights.org/course/)

------
tathougies
The difference big data/data science/empirical study and 'classical' economics
(by which I'd include any system of economics that seeks to explain human
behavior via an underlying metatheory) is that a primarily empirical approach
obscures the necessary underlying theory present in any experiment where
you're trying to fit data to a curve.

For example, when you run a science experiment and you plot the data, you may
find that you're looking at a line. While this is an interesting finding, it
has zero predictive value for anything other than the exact situation you've
collected data for. In order to formulate scientific law, you first must (a)
believe that such a thing exists and (b) have some theory as to what shape the
curve ought to fit. For example, a naive look at physics using an 'empirical'
approach might incorrectly conclude that force is mass times acceleration.
While moderately useful for many problems, this offers little predictive power
in the general case. In order to actually formulate a law that can be of
predictive value, you have to first consider various other laws and axioms
(such as the constant speed of light for force), at which point -- by
deduction, without any need of empirism -- you determine that this is wrong,
and you need another kind of equation to fit your data to.

I don't know if the simplistic demand curves drawn in the original text book
are correct or not. However, at least those are based on a particular set of
assumptions that can be validated or not. The kind of empiricism put forth by
Mr Chetty does not offer this at all.

All this is to say that, while data is useful for validation, it is not useful
for prediction. The last thing we need is a black-box machine learning model
to make major economic decisions off of. What we do need is proper models that
are then validated, which don't necessarily need 'big data.'

~~~
westurner
> _All this is to say that, while data is useful for validation, it is not
> useful for prediction. The last thing we need is a black-box machine
> learning model to make major economic decisions off of. What we do need is
> proper models that are then validated, which don 't necessarily need 'big
> data.'_

Hand-wavy theory - predicated upon physical-world models of equillibrium which
are themselves classical and incomplete - without validation is preferable to
empirical models? Please.

Estimating the predictive power of some LaTeX equations is a different task
than measuring error of a trained model.

If the model does not fit all of the _big_ data, the error term is higher;
regardless of whether the model was pulled out of a hat in front of a captive
audience or deduced though inference from actual data fed through an unbiased
analysis pipeline.

If the 'black-box predictive model' has lower error for all available data,
the task is then to reverse the model! Not to argue for unvalidated theory.

Here are a few discussions regarding validating economic models, some
excellent open econometric lectures (as notebooks that are unfortunately not
in an easily-testable programmatic form), the lack of responsible validation,
and some tools and datasets that may be useful for validating hand-wavy
_classical_ economic theories:

"When does the concept of equilibrium work in economics?"
[https://news.ycombinator.com/item?id=19214650](https://news.ycombinator.com/item?id=19214650)

> "Lectures in Quantitative Economics as Python and Julia Notebooks"
> [https://news.ycombinator.com/item?id=19083479](https://news.ycombinator.com/item?id=19083479)
> (data sources (pandas-datareader, pandaSDMX), tools, _latex2sympy_ )

That's just an equation in a PDF.

(edit) Here's another useful thread: "Ask HN: Data analysis workflow?"
[https://news.ycombinator.com/item?id=18798244](https://news.ycombinator.com/item?id=18798244)

~~~
em500
Most of the interesting economic questions are inference problems, not
prediction problems The question is not "what is the best guess of y[i] given
these values of x[i]'s", but what would y[i] have been for this very
individual i (or country in macro-economics) if we could have wound back the
clock and change the values of x[i]'s for this individual. The methods that
economists know and use may not be the best, but the standard ML prediction
methods do not address the same questions, and data scientists without a
social / economic / medical background are often not even aware of the
distinction.

Economists and social scientists try to do non-experimental causal inference.
Maybe they're not good at it, maybe the very problem is unsolvable, but it's
not because they don't know how Random Forests or RNNs work. Economists
already _know_ that students from single parent families do worse at school
than from married families. If the problem is just to predict individual
student results, number of parents in the household is certainly a good
predictor. The problem facing economists is, would encouraging marriage or
discouraging diveroce improve student results? Nothing in PyTorch or
Tensorflow will help with the answer..

~~~
westurner
Backtesting algorithmic trading algorithms is fairly simple: what actions
would the model have taken given the available data at that time, and how
would those trading decisions have affected the single objective dependent
variable. Backtesting, paper trading, live trading.

Medicine (and also social sciences) is indeed more complex; but classification
and prediction are still the basis for making treatment recommendations, for
example.

Still, the task really is the same. A NN (like those that Torch, Theano,
TensorFlow, and PyTorch produce; now with the ONNX standard for neural network
model interchange) learns complex relations and really doesn't care about
causality: minimize the error term. Recent progress in reducing the size of NN
models e.g. for offline natural language classification on mobile devices has
centered around identifying redundant neuronal connections ("from 100GB to
just 0.5GB"). Reversing a NN into a far less complex symbolic model (with
variable names) is not a new objective. NNs are being applied for feature
selection, XGBoost wins many Kaggle competitions, and combinations thereof
appear to be promising.

Actually testing second-order effects of evidence-based economic policy
recommendations is certainly a complex highly-multivariate task (with
unfortunate ideological digression that presumes a higher-order understanding
based upon seeming truisms that are not at all validated given, in many
instances, _any_ data). A causal model may not be necessary or even reasonably
explainable; and what objective dependent variables should we optimize for?
Short term growth or long-term prosperity with environmental sustainability?

... "Please highly weight _voluntary_ sustainability reporting metrics along
with fundamentals" when making investments and policy decisions?

Were/are the World3 models causal? Many of their predictions have subsequently
been validated. Are those policy recommendations (e.g. in "The Limits to
Growth") even more applicable today, or do we need to add more labeled data
and "Restart and Run All"?

...

From
[https://research.stlouisfed.org/useraccount/fredcast/faq/](https://research.stlouisfed.org/useraccount/fredcast/faq/)
:

> _FREDcast™ is an interactive forecasting game in which players make
> forecasts for four economic releases: GDP, inflation, employment, and
> unemployment. All forecasts are for the current month—or current quarter in
> the case of GDP. Forecasts must be submitted by the 20th of the current
> month. For real GDP growth, players submit a forecast for current-quarter
> GDP each month during the current quarter. Forecasts for each of the four
> variables are scored for accuracy, and a total monthly score is obtained
> from these scores. Scores for each monthly forecast are based on the
> magnitude of the forecast error. These monthly scores are weighted over time
> and accumulated to give an overall performance._

> _Higher scores reflect greater accuracy over time. Past months '
> performances are downweighted so that more-recent performance plays a larger
> part in the scoring._

The #GobalGoals Targets and Indicators may be our best set of variables to
optimize for from 2015 through 2030; I suppose all of them are economic.

~~~
zwaps
Using predictive models for policy is not new, in fact it was the standard
approach long before more inferential models, and the famed Lucas critique
precisely targets a primitive approach similar to what you are proposing.

The issue is the following: In economics, one is interested in an underlying
parameter of a complex equilibrium system (or, if you wish, a non-equilibrium
complex system of multi-agentic behavior). This may be, for example, some
pricing parameter for a given firm - say - how your sold units react to
setting a price.

Economics faces two basic issues:

First, any predictive model (like a NN or simple regression) that takes price
as an input, will not correctly estimate the sensitivity of revenue to price.
It is actually usually the case, that the inference is reversed.

A model where price is input, and sold units or revenue is output (or vice-
versa) will predict (you can check that using pretty much any dataset of
prices and outputs) that higher prices lead to higher outputs, because that is
the association in the data. Of course we know that in truth, prices and
outputs are co-determined. They are simultaneous phenomena, and regressing one
on the other is not sufficient to "causally identify" the correct effect.

This is independent of how sophisticated your model is otherwise. Fitting a
better non-linear representation does not help.

The solution is of course to reduce down these "endogenous" phenomena to their
basic ingredients. Say you have cost data, and some demand parameters. Then,
using a regression model (or NN) to predict the vector of endogenous outcome
variables will work, and roughly give you the right inference.

Then, as a firm, you are able to use these (more) exogenous predictive
variables to find your correct pricing.

This is not new, pops up everywhere in social science, is the basis of a
gigantic literature called econometrics, and really has nothing to do with how
you do the prediction.

The only thing that NN add are better predictions (better fitting) and the
ability to deal with more data. As this inferential problem shows, using more
(and more fine-grained) data is indeed crucial to predicting what a firm
should do.

BUT, it is crucial to understand and reason about the underlying causality
FIRST, because otherwise even the most sophisticated statistical approach will
simply give you wrong results.

Secondly, the counterfactual data for economic issues is usually very scarce.
The approach taken by machine learning is problematic, not only because of
potentially wrong inference, but also because two points in time may simply
not be based on comparable data-generating processes.

In fact, this is exactly the blindness that led to people missing the
financial crisis. Of course, with enough data, and long enough samples, one
should expect to be become pretty good at predicting economic outcomes. But
experience has shown that in economics, these data are simply too scarce. The
unobserved variation between two quarters, two years, two countries, two firms
(etc.) is simply very large and has fat tails. This leads to spontaneous
breakdowns of such predicitive models.

Taking these two issues together, we see that better non-linear function
approximation is not the solution to our problems. Instead, it is a
methodological improvement that must be used in conjunction with what we have
learned about causality.

Indeed the literature moves into a different direction. Good economic science
nowadays means to identify effects via natural experiments and other exogenous
shifts that can plausibly show causality.

Of course such experiments are more rare, and more difficult, the larger the
scale becomes. Which is why Macroeconomics is arguably the "worst science" in
economics, while things like auctions and microstructure of markets are
actually surprisingly good science (nowadays).

Doors are wide open for ML techniques, but really only to the point that they
are useful in operationalizing more and better data.

Anyone trying to understand economic phenomena needs to be keenly aware of how
inference can be done, which requires an understanding (or an approach to) -
that is, a theory - of the underlying mechanisms.

~~~
westurner
Yes, some combination of variables/features grouped and connected with
operators that correlate to an optima (some of which are parameters we can
specify) that occurs immediately or after a period of lag during which other
variables of the given complex system are dangerously assumed to remain
constant.

> _In fact, this is exactly the blindness that led to people missing the
> financial crisis_

ML was not necessary to recognize the yield curve inversion as a strongly
predictive signal correlating to subsequent contraction.

An NN can certainly learn to predict according to the presence or magnitude of
a yield curve inversion and which combinations of other features.

\- [ ] Exercise: Learning this and other predictive signals by cherry-picking
data and hand-optimizing features may be an extremely appropriate exercise.

"This field is different because it's nonlinear, very complex, there are
unquantified and/or uncollected human factors, and temporal"

Maybe we're not in agreement about whether AI and ML can do causal inference
just as well if not better than humans manipulating symbols with human
cognition and physical world intuition. The time is nigh!

In general, while skepticism and caution are appropriate, many fields suffer
from a degree of hubris which prevents them from truly embracing stronger AI
in their problem domain. (A human person cannot mutate symbol trees and
validate with shuffled and split test data all night long)

> _Anyone trying to understand economic phenomena needs to be keenly aware of
> how inference can be done, which requires an understanding (or an approach
> to) - that is, a theory - of the underlying mechanisms._

I read this as "must be biased by the literature and willing to disregard an
unacceptable error term"; but also caution against rationalizing blind
findings which can easily be rationalized as logical due to any number of
cognitive biases.

Compared to AI, we're not too rigorous about inductive or deductive inference;
we simply store generalizations about human behavior and predict according to
syntheses of activations in our human NNs.

If you're suggesting that the information theory that underlies AI and ML is
insufficient to learn what we humans have learned in a few hundred years of
observing and attempting to optimize, I must disagree (regardless of the
hardness or softness of the given complex field). Beyond a few
combinations/scenarios, our puny little brains are no match for our
department's new willing AI scientist.

~~~
zwaps
> ML was not necessary to recognize the yield curve inversion as a strongly
> predictive signal correlating to subsequent contraction.

> An NN can certainly learn to predict according to the presence or magnitude
> of a yield curve inversion and which combinations of other features.

> \- [ ] Exercise: Learning this and other predictive signals by cherry-
> picking data and hand-optimizing features may be an extremely appropriate
> exercise.

If the financial crisis has not yet occurred, how will the NN learn a
relationship that does not exist in the data?

The exercise of cherry picking data and hand-optimizing is equivalent to
applying theory to your statistical problem. It is what is required if you
lack data points - using ML or otherwise. Nevertheless, we (as in humans) are
bad at it. Speaking of the financial crisis. It was not AI's that picked up on
it, it was some guys applying sophisticated and deep understanding of causal
relationships. And that so few people did this, shows how bad we humans are at
doing this implicitly and automatically by just looking at data!

> Maybe we're not in agreement about whether AI and ML can do causal inference
> just as well if not better than humans manipulating symbols with human
> cognition and physical world intuition. The time is nigh! In general, while
> skepticism and caution are appropriate, many fields suffer from a degree of
> hubris which prevents them from truly embracing stronger AI in their problem
> domain. (A human person cannot mutate symbol trees and validate with
> shuffled and split test data all night long)

ML and AI certainly can do causal inference. But then you have to do causal
inference. Again, prediction on historical data is not equivalent to causal
analysis, and neither is backtesting or validation. At the end of the day, AI
and ML improves on predictions, but the distinction of causal analysis is a
qualitative one.

> I read this as "must be biased by the literature and willing to disregard an
> unacceptable error term"; but also caution against rationalizing blind
> findings which can easily be rationalized as logical due to any number of
> cognitive biases.

No. My point is that for causal analysis, you have to leverage assumptions
that are beyond your data set. Where these come from is besides the point. You
will always employ a theory, implicitly or explicitly.

The major issue is not the we use theories, but rather that we might do it
implicitly, hiding the assumptions about the DGP that allows causal inference.
This is where humans are bad. Theories are just theories. With precise
assumptions giving us causal identification, we are in a good position to
argue where we stand.

If we just run algorithms without really understand what is going on, we are
just repeating the mistakes from the last forty years!

> If you're suggesting that the information theory that underlies AI and ML is
> insufficient to learn what we humans have learned in a few hundred years of
> observing and attempting to optimize, I must disagree (regardless of the
> hardness or softness of the given complex field). Beyond a few
> combinations/scenarios, our puny little brains are no match for our
> department's new willing AI scientist.

All the information theory I have seen in any of the Machine Learning
textbooks I have picked up is methodologically equivalent to statistics. In
particular, the standard textbooks (Elements, Murhpy etc.) treatment of
information theory would only allow causal identification under the exact same
conditions that the statistics literature treats.

I fail to see the difference, or what AI in particular adds. The issue of
causal inference is a "hot topic" in many fields, including AI, but the
underlying philosophical issues are not exactly new. This includes information
theory.

You seem to think that ML has somehow solved this problem. From my reading of
these books, I certainly disagree. Causal inference is certainly POSSIBLE -
just as in statistics, but ML doesn't give it to you for free!

In particular, note the following issue: To show causal identification, you
need to make assumptions on your DGP (exogenous variation, timing, graphical
causal relations ... whatever). Even if these assumptions are very implicit,
they do exist. Just by looking at data, and running a model, you do not get
causal inference. It can not be done "within" the system/model.

If you bake these things into your AI, then it, too makes these assumptions.
There really is no difference. For example, I could imagine an AI that can
identify likely exogenous variations in the data and use them to predict
counterfactuals. That's probably not too far off, if it doesn't exist already.
But, this is still based on the assumption that these variations are, indeed
exogenous, which can never be proven within the DGP.

In contrast, I find that most "AI scientists" care very much about prediction,
and very little about causal inference. I don't mean this subfield doesn't
exist. But it is a subfield. In contrast, for many non-AI scientists, causal
inference IS the fundamental question, and prediction is only an afterthought.
ML in practice involved doing correct experiments (AB testing), at best. It
will sooner or later also adopt all other causal inference techniques. But, my
point stands, I have yet to see what ML adds. Enlighten me!

AI, ML and stats will merge, if they haven't already. The distinction will
disappear. I believe the issues will not. I employ a lot of AI/ML techniques
in my scientific work. Never have they solved the underlying issue of causal
inference for me!

~~~
westurner
A causal model is a predictive model. We must validate the error of a causal
model.

Why are theoretic models hand-wavy? "That's just because noise, the model is
correct." No, such a model is insufficient to predict changes in dependent
variables when in the presence of noise; which is always the case. How does
validating a causal model differ from validating a predictive model with
historical and future data?

Yield-curve inversion as a signal can be learned by human and artificial NNs.
Period. There are a few false positives in historical data: indeed, describe
the variance due to "noise" by searching for additional causal and correlative
relations in additional datasets.

I searched for "python causal inference" and found a few resources on the
first page of search results:
[https://www.google.com/search?q=python+causal+inference](https://www.google.com/search?q=python+causal+inference)

CausalInference:
[https://pypi.org/project/CausalInference/](https://pypi.org/project/CausalInference/)

DoWhy:
[https://github.com/microsoft/dowhy](https://github.com/microsoft/dowhy)

CausalImpact (Python port of the R package):
[https://github.com/dafiti/causalimpact](https://github.com/dafiti/causalimpact)

"What is the best Python package for causal inference?"
[https://www.quora.com/What-is-the-best-Python-package-for-
ca...](https://www.quora.com/What-is-the-best-Python-package-for-causal-
inference)

Search: graphical model "information theory" [causal]
[https://www.google.com/search?q=graphical+model+%22informati...](https://www.google.com/search?q=graphical+model+%22information+theory%22)

Search: opencog causal inference
[https://www.google.com/search?q=opencog+causal+inference](https://www.google.com/search?q=opencog+causal+inference)
(MOSES, PLN,)

If you were to write a pseudocode algorithm for an econometric researcher's
process of causal inference (and also their cognitive processes (as executed
in a NN with a topology)), how would that read?

(Edit) Something about the sufficiency of RL (Reinforcement Learning) for
controlling cybernetic systems.
[https://en.wikipedia.org/wiki/Cybernetics](https://en.wikipedia.org/wiki/Cybernetics)

~~~
em500
What's the point of dumping a bunch of Google results here? At least half the
results are about implementations of pretty traditional etatistical /
econometric inference techniques. The Rudin causal inference framework
requires either randomized controlled trials or for propensity score models an
essentially unverifiable separate model step.

Google's CausalImpact model, despite having been featured on Google's AI blog,
is a statistical/econometric model (essentially the same as
[https://www.jstor.org/stable/2981553](https://www.jstor.org/stable/2981553)).
It leaves it _up to the user_ to find and designate a set of control
variables, which has to be _designated by the user_ to be unaffected by the
treatment. This is not done algorithmically, and has very little to do with
RNNs, Random Forests or regression regularization.

> If you were to write a pseudocode algorithm for an econometric researcher's
> process of causal inference (and also their cognitive processes (as executed
> in a NN with a topology)), how would that read?

[1] Set up a proper RCT, that is randomly assign the treatment to different
subjects [2] Calculate the outcome diffences between the treated and untreated

For A/B testing your website, the work division between [1] and [2] might be
50-50, or at least at similar order of magnitudes.

For the questions that academic economists wrstle with, say, estimate the
effect of increasing school funding / decreasing class size, the effect of
shifts between tax deductions vs tax credits vs changing tax rates or bands,
or of the different outcome on GDP growth and unemployment of monetary vs
fiscal expansion [1] would be 99.9999% of the work, or completely impossible.

Faced with the impracticallity/impossiblility of proper experiments, academic
micro-economists have typically resorted to Instrumental Variable regressions.
AFAICT finding (or rather, convincing the audience that you have) a proper
instrument is not very amendable to automation or data mining.

In academic macro-economics (and hence at Serious Institutions such as central
banks and the IMF), the most popular approaches to building causal models in
the last 3 or 4 decades have probably been 1) making a bunch of unrealistic
assumpsions of the behaviour individual agents (microfoundations/DSGE models)
2) making a bunch of uninterpretable and unverifyable technical assumptions on
the parameters in a generic dynamic stochastic vector process fitted to macro-
aggregates (Structural VAR with "identifying restrictions") 3) manually
grouping different events in different countries from different periods in
history as "similar enough" to support your pet theory: lowering interest
rates can lead to a) high inflation, high unemployment (USA 1970s), b) high
inflation, low unemployment (Japan 1970s), b) low inflation, high unemployment
(EU 2010s) c) low inflation, low unemployment (USA, Japan past 2010s)

I really don't see how a RL would help with any of this. Care to come up with
something concrete?

~~~
westurner
> _What 's the point of dumping a bunch of Google results here? At least half
> the results are about implementations of pretty traditional etatistical /
> econometric inference techniques._

Here are some tools for causal inference (and a process for finding projects
to contribute to instead of arguing about insufficiency of AI/ML for our very
special problem domain here). At least one AGI implementation doesn't need to
do causal inference in order to predict the outcomes of actions in a noisy
field.

Weather forecasting models don't / don't need to do causal inference.

> _A /B testing_

Is multi-armed bandit feasible for the domain? Or, in practice, are there too
many concurrent changes in variables to have any sort of a controlled
experiment. Then, aren't you trying to do causal inference with mostly
observational data.

> _I really don 't see how a RL would help with any of this. Care to come up
> with something concrete?_

The practice of developing models and continuing on with them when they seem
to fit and citations or impact reinforce is very much entirely an exercise in
RL. This is a control system with a feedback loop. A "Cybernetic system". It's
not unique. It's not too hard for symbolic or neural AI/ML. Stronger AI _can_
or _could_ do [causal] inference.

------
bubblewrap
"They were teaching their students big ideas. But they were ideas about what
causes what — not about supply and demand."

Because supply and demand doesn't cause things? I think there are some
citations needed for that claim.

Reading between the lines, I think the VOX article makes the "new approach"
sound more stupid than it actually is. It is of course a good idea to test
economic ideas with rigorous methods.

Article makes it sound almost as if the "new economics" was just "grievance
studies" \- creating statistics about how disadvantaged some people are (as if
society and economics have been previously unaware that such people exist).
That would be stupid, because it doesn't teach you anything about what you
could possibly do about it. But between the lines, the professor seems to
conduct experiments to determine actual outcomes of economic measures. That
makes sense. But you still need "normal economics" to come up with measures
that have a shot at improving things.

------
js8
It's about time! The equilibrium theory of supply and demand has set economics
back for decades. There is literally a century of criticism of the theory from
postkeynesians, not only the lack of dynamism, but also all the strange
assumptions.

~~~
brownbat
People keep saying supply and demand is a broken theory, but I'm not sure how
to interpret that.

When oil prices fall some producers seem to shut off their pumps. When
restaurants raise their prices the people I know tend to cook at home more.

Do you think those are weird outliers, and increasing prices should spur
increasing purchases as a general rule? Do you think consumer and producer
behaviors have no relationship with price whatsoever?

I have a hard time imagining what those worlds would look like, so I expect
your critique is far more nuanced.

But if it is a nuanced critique, then it seems we all agree on the general
principles in broad strokes, and we don't need to throw out supply and demand
after all.

~~~
atq2119
The biggest problem is with the supply curve, really. Economics tends to
pretend that more supply requires a higher price. In reality, more supply very
often leads to a _lower_ price due to economies of scale.

This is a fundamental problem because it means that even if you assume fixed
supply and demand curves (which is very dubious), there can be more than one
equilibrium. That in itself pretty much invalidates most of the standard
subsequent analysis.

The other big problem is that it assumes too much that all economic actors are
price takers. In reality, prices are largely administered via cost-plus
pricing, and advertisement and related tricks are used extensively to subvert
the basic principles of supply and demand.

Another problem specific to macroeconomics is that it largely ignored the
effect of demand. Increased demand very often leads to an increase in
production rather than an increase of prices, a fact that was largely ignored
in decades of supply-centred thinking. This has led to bad policies in
response to the global financial crisis, for example.

I agree though that it makes no sense to throw out supply and demand as
concepts entirely.

~~~
barry-cotter
Newaccount456 has already covered your confusion over the difference between a
movement of and a shift along the supply curve adequately. That entirely
covers the more than one equilibrium point because even in Econ 101 they tell
you the curves shift. Of course there are multiple equilibria. Increasing,
decreasing and constant economies of scale will be in every intro textbook.

Re: Price taking or perfect competition, that’s just one of the basic models
of price determination. Monopoly, monopsony, oligopoly and monopolistic
competition are all treated in introductory courses too and if you got as far
as an intermediate course they’d cover the Bertrand, Cournot and Stackleberg
models of oligopolistic competition and how they contrast with perfect
competition and monopoly. Even introductory microeconomics will cover the
three basic methods of price determination verbally.

Your penultimate paragraph also relies on not being able to distinguish
between a movement of a demand curve and a shift along an existing one.

Everything you wrote is covered in intro micro.

~~~
js8
> Newaccount456 has already covered your confusion over the difference between
> a movement of and a shift along the supply curve adequately.

I think you're wrong, nobody is confused about shift of the curves. The
problem really is, there can be multiple equilibriums even if the curves stay
the same.

It is kind of difficult to see and understand why, because the traditional
supply/demand theory and diagrams obscure this heavily. But if you look at the
problem differently (see my other reference to Blatt), and start taking into
account several products at the same time, you will see why.

~~~
atq2119
This is exactly it. The single equilibrium hinges on the assumption about the
monotonicity and slopes of supply and demand. But these assumptions are simply
wrong, and so multiple equilibria are possible even with a single market.
Multiple markets of course make the problem worse.

------
adamnemecek
> Mankiw’s textbook covers the abstract theory that underpins economics as it
> has been understood for decades. It is about supply and demand, about how
> prices can be used to match production of a good to its consumption, and
> about the power of markets as a tool for allocating scarce resources.

Few things are as abstract as supply and demand.

------
MrTonyD
I've worked with lots of Harvard graduates over the years. They all seemed to
be cloned with some kind of "group-think" about Economics. Essentially, they
were all convinced that markets solved big problems, and anything vaguely
Keynesian or Socialist or Marxist was doomed to failure.

So I guess I view Harvard Economics as a tool of those with power, and
probably something which has contributed to creating a worse world. (Hey, I
believe markets have benefits too, but not to the point of dismissing
moderating policies.)

------
carlosgg
Vox article:

[https://www.vox.com/the-
highlight/2019/5/14/18520783/harvard...](https://www.vox.com/the-
highlight/2019/5/14/18520783/harvard-economics-chetty)

~~~
dang
That gives more background, so we've changed the url to it from
[https://opportunityinsights.org/course/](https://opportunityinsights.org/course/).
Thanks!

------
camdenlock
It’s hard not to be cynical when I see a link to vox.com, especially when my
cynical assumptions end up being correct.

“Please don’t be about revolutionizing economics via identitarianism... <taps
link> ... sigh...”

~~~
jonas21
Did we read the same article? The one I read is about changing how economics
is taught by treating it more like a data-driven empirical science.

~~~
jnordwick
Keep reading. It goes so far as to hold Card up as some master of this new
data-oriented movement - the guy massages it better than the girl offering you
a happy ending. And it goes on to highlight other orthodox turned radical
anti-capitalists.

Anybody skilled enough can take a sample of data and play with it enough -
fill in holes with other data - exclude some points - until they get the story
they want. To some extent there needs to be a mechanism (beside "people are
stupid") to tie things together. And this has been the general MO of left-wing
economists. They lost ideas and theory so they moved to numbers. The numbers
tend to not be predictive because they are so overly played with, so obviously
everybody is just being irrational - the world is wrong not their theory
because they proved it right with the data.

It is Vox after all - a self proclaimed socialist website that has given page
space to the the Jacobins on how Democratic Socialism isn't social democracy -
it is only a starting point for pure socialism.

~~~
lern_too_spel
Card doesn't rely on irrational firms. He showed with data that labor demand
is inelastic at minimum wage in the region studied. When this is true, the
benefits of increasing minimum wage outweigh the costs.

~~~
jnordwick
On that infamous study he used phone calls that had very strange results like
doubling number of employees or going to entirely fulltime. When the study was
redone with payroll reports, the results reversed - back to the start minimum
wage effect.

So then they republished and it was like "well if we only look at this group
and we take out these and adjust this number" they were able to recover their
results but the data hacking was pretty terrible.

If you ate going to write a study like that, relying on phone data screams
bias (and these were two economists that couldn't claim they didn't know
better).

~~~
lern_too_spel
Citation, please? Card's matching analysis has been repeated many times (e.g.,
Dube, et al), and each time, they have found little demand elasticity for
labor at the extremely low end.

------
fromthestart
>That shift could change economics itself, by attracting a new breed of
students who are intrigued by the field’s new empiricism, not put off by its
mathiness and high theory. It could make economics departments more diverse,
and more open to new perspectives from women and students of color.

>He also gives Mankiw credit for moving the curve of Ec 10 to match the curves
of other large Harvard classes, based on research showing that unnecessarily
tough grading of economics classes disproportionately discourages women from
taking them.

I do not understand how attracting members of underrepresented classes
benefits them when standards need to be lowered to do so. Sure, the practice
opens up opportunities to a more diverse sampling of individuals in the short
term, but in the long term you devalue the credentials and, as bad or worse,
you risk churning out graduates who are more qualified on paper than in
reality.

I feel like this is a dangerous, growing trend in modern Western society and I
do not understand how nobody seems to see it as a problem.

~~~
WoahNoun
That's not what that quote implies. It implies that underrepresented
demographics were less likely to enroll in classes that were known as having
harsher grading curves than other classes. It doesn't imply that the standards
for passing the class were lowered or the performance of underrepresented
demographics was lower than non-underrepresented demographics once they
enrolled in the course.

~~~
fromthestart
>It doesn't imply that the standards for passing the class were lowered

That's literally what it implies. The curve was relaxed. It became easier to
meet the standard of a minimum pass grade for a given level of performance.

> or the performance of underrepresented demographics was lower than non-
> underrepresented demographics once they enrolled in the course

Nor do I. But lower standards (easier to pass/earn a high grade) implies
poorer qualification/ability.

Curves by definition bring up the grades of those who perform worse, without
changing how they perform. In this example, if we peel away the euphemism,
women were deterred from enrolling because the class was too hard to pass, so
it was made easier to pass without changing the content. That does not
necessarily mean that the women now taking the course are poor performers, but
it does mean that on average the course will be graduating more students who
would not have met previous standards.

~~~
WoahNoun
>That's literally what it implies. The curve was relaxed. It became easier to
meet the standard of a minimum pass grade for a given level of performance.

A curve can be shifted without changing the standards for passing the class.
Eg the original curve allowed only for 5% of the students to get A's while the
new curve allows 10%. You can get that additional 5% by shifting B's, C's, and
D's upward which doesn't change the cutoff for failing with an F.

>Curves by definition bring up the grades of those who perform worse

Curves can also drag down students who are performing at a high level. If 10%
of the class has performance deserving of A's but the curve says only 5% of
the class can have an A, then some group is getting B's instead.

