Share this page!

Judea Pearl Profile picture
Twitter logo
15 Jan, 4 tweets, 1 min read
A letter I wrote to the California Board of Education:

I strongly oppose the 2021 California Ethnic Studies Model Curriculum.

I am particularly alarmed by its attempt to depict inter-ethnic relationships as a irreconcilable struggle between racially-defined “oppressed” 1/4
and "oppressors” and by the way it associates "whiteness" with "oppression" and "colonialism".

I am a "white" Jewish American, and I believe that the history of my people is a model of emancipation from oppression and colonialism, culminating in the State of Israel which is 2/4
an inspirational model of an oppressed ethnic minority lifting itself from the margin of history to become a world center of art, science and entrepreneurship -- a multi-colored light-house of free speech and gender equality.

I want my grandchildren to take pride in this 3/4
historical transformation and to share our experience with other minorities; yet sharing as equal partners in one colorful mosaic of ethnic diversity, not as guilt-stricken "Whites", burdened with undeserved privileges. 4/4

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Judea Pearl

Judea Pearl Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @yudapearl

Judea Pearl Profile picture
6 Nov 20
This question annoys ALL students (and professors) of ML, but they are afraid to ask. Thanks for raising it in this "no hand waving" forum. Take two causal diagrams:
X-->Y and X<--Y, and ask a neural network to decide which is more probable, after seeing 10 billion samples. 1/n
The answer will be: No difference; each diagram scores the same fit as the other. Let's be more sophisticated: assign each diagram a prior and run a Bayesian analysis on the samples. Lo and Behold, the posteriors will equal to the priors no matter how we start. How come? 2/n
Isn't a neural network supposed to learn the truth given enough data? Ans. No! Learning only occurs when the learnable offends the data less than its competitors. Our two diagrams never offend any data, so nothing is learnable. Aha! But what if our data involves interventions? 3/
Read 6 tweets
Judea Pearl Profile picture
27 Oct 20
When I see a paper on explainability, first question I
ask is: "What does it explain?", the data-fitting strategy of
the fitter? or real-life events such as death or survival.
I believe this paper arxiv.org/pdf/2010.10596…
is mostly about the former, as can be seen from the 1/
equations and from the absence of any world-model.
While it is sometimes useful to explain the data-fitting system (eg. for debugging), it is also important to distinguish this kind of counterfactual explanations
from the kind generated in the causal inference literature.
2/3
Beware, a model-blind system might conclude that
the rooster crow explains the sunrise. It might also explain that your loan was denied because you are a male, and would also have been denied if you were a female. I wonder how ML folks would debug this system.
Read 4 tweets
Judea Pearl Profile picture
20 Jun 20
1/4 Comments on your Front-Door paper:
* The expression "a single, strictly exogenous mediator
variable" is problematic: (1) Causality p. 82 defines
FDC as "A set of variables", not "a single variable". (2)
"exogenous mediator" is an oxymoron. I originally
called it (1973):
2/ "Mediating Instrumental Variables"
ucla.in/2pJzGNK, best described as an "exogenously-disturbed mediator".

* "The first application of FDC" sounds too pessimistic. Situations involving exogenously-disturbed mediators are at least as plausible as "exclusion-restricted
3/ exogenous variables" (traditional IV's) which were introduced 70 yrs earlier, when DAGs were not
around to invite scrutiny. Imbens comments
reflect that absence ucla.in/36EoNzO

* Why introduce FDC in the context of linear regression
where ATE is identifiable by
Read 4 tweets
Judea Pearl Profile picture
28 Mar 20
1/ I'm glad, Sean, that our brief exchange has resulted in your great clarification of the issues, from which I have learned a lot. Two thoughts come immediately to mind: (1) It is a blessing that we can enjoy a division of labor between CI and statistics, the former generates
2/ causal estimands, the latter estimate them. Note though that the former is not totally oblivious to the type of data available. Different types of data will result in different estimands. eg.,experimental vs. observational, corrupted by missingness or by proxies or by
3/ differential selection etc. (2) I don't buy the mystification of "collecting adequate data". I am in the business of automating a scientist, so, if there is human judgement involved in the data collection task, we do not stop here and surrender it to humans. We see it as an
Read 4 tweets
Judea Pearl Profile picture
16 Dec 19
1/4 In view of the dominant role that re-weighing plays in
extrapolating effects across populations, and the many
Twitter requests for a concise graphical criterion
that gives re-weighing its legitimacy, I am retweeting
the criterion (called "S-admissibility"), in next 4 tweets.
2/ It works on a selection diagram in which S nodes represent disparities between the target (*) population and study population (experimental). Z is a set of measurements. To test if Z is S-admissible
(1) Remove all arrows pointing to X
(2) Check if {X, Z} d-separates S from Y
3/4 If Z passes this test, then the reweighing formula is valid:
P*(y|do(x)) = SUM_z P(y|do(x),z)P*(z)
In words: Effect at target equals the Z-specific effects at study, averaged over Z, using the target distribution P*(z) as weight.
Warning, this is merely a sufficient test.
Read 4 tweets
Judea Pearl Profile picture
31 Aug 19
1/3 This is an excellent paper, that every regression analyst should read. Primarily, to appreciate how problems that have lingered in decades of confusion can be untangled today using CI tools. What I learned from it was that the "suppressor surprise" is surprising even when
2/3 cast in a purely predictive context: "How can adding a second lousy predictor make the first a better predictor?" Evidently, what people expect from predictors clashes with the logic of regression slopes. The explanation I offered here ucla.in/2N8mBMg (Section 3)
3/3 shows how the phenomenon comes about, but the reason for the clash is still puzzling: What exactly do people expect from predictors, and why? #Bookofwhy
Read 6 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

Like this author's thread?

Get emails when @yudapearl has new unrolls!

Check out other threads from @yudapearl!

Read all threads by @yudapearl