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31 Jul, 4 tweets, 1 min read
1/ Can "traditional statistics" handle "effect sizes?" If we include Neyman-Rubin in "traditional statistics" and interpret "Can" to mean "Can, in principle", the answer is Yes. However, if we take "traditional statistics" to be represented by: Pearson, Fisher, Chochran, Tuckey,.
2/ Breiman, Friedman,...+deceased presidents of ASA, RSS...+authors of stat texts+..., and if we interpret "Can" to mean "Capable of handling a simple problem in 2 weeks time," I would bet 100:1 on "NO!". Reason: They lacked a language to articulate the assumptions needed for
3/ estimating effect sizes, and it takes about 2 weeks to learn such a language, be it "potential outcomes", DAGs, SCM, or equivalent. Why haven't the giants bothered to learn any? My 1993-9 email is full of reasoned excuses, but the most common one has been: "It takes us out of
4/ traditional probability calculus.." or "Is do(x) a concept in some new theory of probability? Define it! (actual quote)". As T. Kuhn said: "Never ask a scientist to jettison the paradigm that has been the basis of his/her life's work (paraphrased) you will never be forgiven."

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Judea Pearl Profile picture
5 Jul
1/ Readers ask: What's the simplest problem in which a combination of experimental and observational studies can be shown to be better than each study alone?
Ans. Consider X--->Z----> Y
with unobserved confounder between X & Z.
Query Q: Find P(y|do(x))
We have 2 valid estimands:
2/
ES1 = P(y|do(x)) estimable from the experiment
ES2 =SUM_z P(z|do(x))P(y|z), the first term is estimable from the experiment, the second from the observational study.
ES2 is better than ES1 for 3 reasons:
1. P(y|z) can rest on a larger sample
2. ES2 is composite (see
3/ (see ucla.in/2ocoWqq for advantage of composite estimators)
3. ES2 need not measure Y in the experimental study.
Remark: The validity of ES2 follows from do-calculus. Adding any edge to the graph invalidates ES2 and leaves ES1 the only estimand.
Read 4 tweets
Judea Pearl Profile picture
5 Jul
1/ It might be useful to look carefully at the logic of proving an "impossibility theorem" for NN, and why it differs fundamentally from Minsky/Papert perceptrons. The way we show that a task is impossible in Rung-1 is to present two different data-generating models (DGM) that
2/ generate the SAME probability distribution (P) but assign two different answers to the research question Q. Thus, the limitation is not in a particular structure of the NN but in ANY method, however sophisticated, that gets its input from a distribution, lacking interventions.
3/ This is demonstrated so convincingly through Simpson's paradox ucla.in/2Jfl2VS,
even pictorially, w/o equations, in #Bookofwhy
Thus, it matters not if you call your method NN or "hierarchical nets" or "representation learning", passive observations => wrong answers
Read 4 tweets
Judea Pearl Profile picture
28 Jun
1/ This post re-enforces my support of the Myth of the Lone Genius, as tweeted here: 6.12.2021 (1/ ) Why I refuse to "cancel" or "decolonialize" Euclid Geometry, Archimedes Rule and Newton's Law, despite peers pressure? Because, as explained here ucla.in/2Qg0Rfs (p.8),
2/ putting a human face behind theorems and discoveries makes science "not a book of facts and recipes, but a struggle of the human mind to unveil the mysteries of nature." Personalizing science education makes each student "an active participant in, not a passive recipient of,
3/ those theorems and discoveries". Thus, regardless of how many collaborators Newton had, telling a student that by an effort of intellect, curiosity, and hard labor he/she can discover the one thing missing from the puzzle, we are turning that students into a
Read 4 tweets
Judea Pearl Profile picture
1 Feb
1/ Sharing an interesting observation from Frank Wiltzeck's book "Fundamentals."
In the 17th Century, while the entire scientific
world was pre-occupied with planetary motion and other
grand questions of philosophy, Galileo made careful studies of simple forms of motion, e.g.,
2/ how balls roll down an inclined plane and how pendulum oscillate. To most of Galileo's contemporaries such measurements must have appeared trivial, if not irrelevant, to their speculations on how the world works. Yet Galileo aspired to a different kind of understanding.
3/ He wanted to understand something precisely, rather than everything vaguely. Ironically, it was Galileo's type of understanding that enabled Newton's theory of gravitation to explain "how the world works".

What do I mention it? Because we have had lengthy
discussions here
Read 4 tweets
Judea Pearl Profile picture
15 Jan
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
Read 4 tweets
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

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