Any discussion of "paradoxes" is really an exercise in psychology. Yet we, quantitative analysts, are trying to avoid psychology at all cost. We can't. We must explicate why two strong intuitions seem to clash, and the conditions under which our intuitions fail. See #Bookofwhy
That is why I am begging folks: "Please, do not tell me 'I am not entirely satisfied' before you tell me why you are surprised (by the paradox) ". I am proud that #Bookofwhy addresses this question (of "surprise") head on, before offering "a resolution".
Now, speaking specifically about Lord's paradox, the paradox was introduced to us in "asymptotic" terms (ie, using distributions, not samples) and we were surprised. Is it likely that we can resolve our surprise by going to finite samples? or to "block design"? #Bookofwhy
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Got my first session with GPT-4, amazing! Though it failed its first causal understanding test.
Me: "Is it possible that smoking causes grade increase on the average and, simultaneously, smoking causes grade decrease in every age group?"
GPT-4: It is theoretically possible
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for smoking to cause an average grade increase overall while causing a grade decrease within every age group. However, this scenario would likely involve some form of Simpson's paradox, where the overall relationship between two variables reverses when accounting for
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a third variable (in this case, age). ....etc.
It failed because (1) I asked a question the answer to which is "definitely NO, no exceptions", and (2) It read somewhere about Simpson's Paradox and a "third variable," overlooking that the question was explicitly causal,
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To understand how trialists, under additivity, can get away without causal calculus, it is instructive to see how selection bias cures itself in linear systems. Let's start with
Eq. (10) of ucla.in/2LcpmHz which, for any 3 variables, X,Y,W gives:
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(10) beta_yx = beta_yx.w ( 1-beta_xw^2) + beta_xw * beta_yw
It says that the regression of Y on X can be written in terms of other regression coefficients, each and every one of which is conditioned on w. If we now think of W=1 as the index of selecting units into the study,
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equation (10) implies that the regression of Y on X at the target population can be inferred from regressions estimated in the study, W=1, regardless of how X, Y and W are interacting.
Two distinct cases are worth noting. (1) If W is a pre-treatment variable, as in Fig. 3,
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As the year draws to a close, many are looking back on the moments from 2021 that gave them hope and encouragement. Kenneth Markus lists his 10 Most Inspiring Moments in the fight against #antisemitism: jewishjournal.com/commentary/opi…
I would like to add another, missed by
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the media, suppressed by our leaders, and hush-hushed by university administrators. Yes, I'm coming back to the USC scandal, and the 65 of its top professors who scored a huge victory through this letter: usc-faaz-12-2021.org. Here they defined in effect a new
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minority group on US campuses: Identity Zionism, defined not by victimhood and
need of protection, but by its inspiring nation-building experience and ideas, its academic excellence, and its unique contributions to campus life. To appreciate the magnitude of the victory,
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Is it dumbness or deliberate blindness that prevents USC officials from listening to their students and faculty? Death threats were disseminated against Zionists. Incriminating statements were made against the very being of Israel. 60 distinguished professors are pleading 1/4
with USC leadership to explicitly de-criminalize Zionist and Israeli identities [quoting from their Letter]:
"Most importantly, Jewish, Zionist, and Israeli students, as well as those who support the right of the State of Israel to exist need to hear from our leaders that 2/4
they are welcome on our campus." Yet, stunningly, in their response, USC leaders blatantly and meticulously refrain from spelling out the words "Zionist" and "Israel", leaving thousands of students, faculty, staff, potential students, parents of USC students, and the 3/4
A new book "Causation in Science, by Yemima Ben-Menahem makes the point that, in ordinary scientific practice, conservation constraints often serve as explanations. For example: "Why did the roller coaster slow down"? "Because energy must be conserved" watermark.silverchair.com/fzab078.pdf?to…
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To include such constraints as "causal explanations" Ben-Menachem advocates abandoning the paradigm that causation is a relation between events, or variables. I hesitate! Considering the fact that conversational utterances are in themselves products of language constraints, 2/4
they are hardly in a position to illuminate the nature of causation. To elaborate, all scientific languages, until 1920, were wedded to the symmetric algebraic equality "=", lacking notation for the assignment operator ":=", with which causal asymmetries can be expressed.
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1/5 Finding a do-operator in a @DeepMind article is a tectonic progress that deserves welcoming blessing. The "delusions" treated in this article are endemic of "Evidential Decision Theory" which Causality (ch 4.1.1 ucla.in/38bmhnO)
summarizes in a mnemonic limerick:
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- Whatever evidence an act might provide
- On what could have caused the act,
- Should never be used to help one decide
- On whether to choose that same act.
Typical real life ramifications of these delusions are: (1) patients should avoid going to the doctor “to reduce the
3/5 probability that one is seriously ill”
(2) workers should never hurry to work, to reduce the probability of having overslept, and more.
The deployment of the do-operator eliminates these "delusions" and has led to the "sequential backdoor criterion" of Sec. 4.4.3.