1/ I'm compelled to retweet this thread because so often I see well-intentioned people assuming that a "happy ever after" 1-state solution is inevitable because Palestinian's rejection of Jewish self determination (in ANY borders) is so total and deeply entrenched that any other
2/ arrangement amounts to endless blood shed. As one born in Israel and tuned daily to the country's pulse, let me mention another factor which is often ignored in conversations about the 1-state fantasy. Israelis resistance to a 1-state solution is at least as total and deeply
3/ entrenched as the Palestinians' rejection of Zionism. For Israelis, the idea of relinquishing Statehood (i.e., right to self defense and border control) towers as a collective suicide, against which people are willing to fight to the last soul (barring a few ultra orthodox
4/ enclaves). It is often equated with a betrayal of 2 thousands years of yearning for normalcy and dignity and, therefore, no government would last a day even discussing such an option. This factor must be taken into account when considering the 1-state vs. 2-state options.
5/ The former would not be as bloodless as some people fantasize; it will be genocidal. The current state of painful but manageable violence is probably the least genocidal, and will persist therefore until some "end of claims" ideology emerges from education for co-existence.

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More from @yudapearl

23 Sep
It's now 34-countries boycotting the Durban Conference, but my eyes are still on Norway. Oh Norway, Norway! How could your Gv't face it's people: Sorry, we fell asleep, and found ourselves swimming in the cesspool of civilization. To honor readers whose Governments remained 1/2
morally sober, I am listing the 34 countries that decided to boycott the Zionophobic Durban Conference:
Albania, Australia, Austria, Bulgaria, Canada, Colombia, Croatia, Cyprus, Czech Republic, Dominican Republic, Estonia, France, Georgia, Germany, Greece, Honduras, Hungary, 2/
Read 4 tweets
21 Sep
1/ Summarizing our discussion of "demand" via "ceteris paribus" (CP), we've seen that, once formalized, CP amounts to comparing Y under two settings of X, say X=x and X=x', while leaving other variables in the structural equation for Y unchanged. The beauty of formal definitions
2/ is that they hold for all models and are independent on the meanings of X, Y,Z, etc,
or the procedure by which we estimate things. Leveraging these beauties, we come to realize that the resultant CP definition of "demand" is none other but the counterfactual definition of
3/ Causal Effect, namely {Y(x, Z),Y(x', Z)}, where Z is the set of other variables in the eq. of Y, both observed and unobserved. Thus, the analysis of "demand" can benefit directly from the literature on causal effects, presenting no peculiarities that demand special treatments.
Read 4 tweets
5 Aug
1/ This just in. A new successful paradigm for building AI systems has emerged, called "Foundation Model". According to its inventors
crfm-stanford.github.io, it works as follows: "Train one model on a huge amount of data and adapt it to many applications." Not only is it
2/ seen "as the beginnings of a sweeping paradigm shift in AI", but a whole Center has been erected in its honor, dozens of prominent researchers, post-docs and PhD students has joined its staff, and an interdisciplinary symposium has been announced. We, foot-soldiers in the
3/ trenches of AI research are asking, of course: "What is it?" or "What is the scientific principle by which 'Foundation models' can circumvent the theoretical limitations of data-centric methods as we know them, especially those that hinder generalization across environments?"
Read 4 tweets
31 Jul
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
Read 4 tweets
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
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

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