#Bookofwhy

Quick #DAG #CausalInference #BookOfWhy noob question: I understand d-separated vars should be uncorrelated but does it work the other way around? ie If I observe a null relation btw two variables, (assume perfect parameter estimation) does this mean these vars are d-separated?

plausibility of the claims? (3.4) What would I do with it, once I agree with the plausibility? (3.5) Does it have any testable implications? (3.6) Unlike my semi-revitalized colleagues, I will begin critiquing specific models only when satisfied with (3.1)-(3.5). #Bookofwhy

I thought this argument was settled in the 1990s .. come to think of it, may be a good candidate for #bookofwhy type causal analysis

I would like to believe that defenders of mainstream listen to me, not because I have an authority in any subject, but because my arguments for revitalizing mainstream make good sense and science would benefit from the revitalizatioin. #Bookofwhy

, equally important question: "Suppose it was right, what would we do with it?". Attending to this question is pre-requisite to resolving causal problems such as Lord's Paradox. (or, more generally: should we adjust for base-line conditions?) #Bookofwhy

Perhaps you can explain "the situation" w/o DAGs. But I have not seen an explanation of why we should come to a different decision, with the same data, depending on the story. #Bookofwhy

First, the data show that initial weight is correlated with diet. Second it stands to reason that over-weight students would choose a dining room differently than under-weight. #Bookofwhy

The question was: what information do we need to decide correctly. This can be answered independently of the question "do we have this information?" or "do we have sufficient evidence to support the needed information?" Separating tasks does not mean neglecting tasks #Bookofwhy

Correcting a link to the Lord Paradox posting. The correct link is http://causality.cs.ucla.edu/blog/index.php/2019/08/13/lords-paradox-the-power-of-causal-thinking/ … and it should go to: Lord Paradox and the Power of Causal Thinking. (Thanks to Stephen Leroy for noting). #Bookofwhy

2/ sufficient for resolving the paradox, namely, for deciding if X increases Y for a person with unknown color. I would be happy to respond to anyone who thinks this statement is in some way incomplete. #Bookofwhy

I would say: It is impossible to "deal with Simpson's Paradox" without a causal model, and it is impossible to specify a causal model in the language of probability distributions, however intricate. See https://ucla.in/2Jfl2VS  which you've cited but not taken seriously.#Bookofwhy

I interpret your hesitation to mean: It depends on the causal relationships between X, Y and Color. Agree. Absent these relationships we cannot decide if X would increase or decrease Y for that person. No need to bemoan science, we need only glance at the causal graph. #Bookofwhy

It's the appendix to a prepublication report. It proves that reversal cannot occur in causal logic, in which the notion of "good for men" is expressed in do-operator. And it claims our intuition comes from this calculus, not from statistics, where reversal does occur. #Bookofwhy

A colleague alerted me to a new wikipedia entry on Market Blanket https://en.wikipedia.org/wiki/Markov_blanket … It is badly written, defining MB as a property of a graph, instead of a probability distribution. The most astonishing feature, uniqueness under positivity, is not mentioned. #Bookofwhy

3/3 when I remind them of the date, and their students still can't cope with a paradox that has haunted statistics for the past 120 years. Pearl unfairly bashes statistics, they say. #Bookofwhy

I am no longer convinced this is true, after seeing readers comments on "compatibility among the experts". The only way to grasp differences and commonalities is to take ONE toy problem and try to solve it using competing "approaches". The rest is Hollywood. #Bookofwhy

One argument is, indeed, that it should have been encoded in the SCM. Another, that it should be treated as "disjuctive action", and extrapolated by imaging: https://ftp.cs.ucla.edu/pub/stat_ser/r359-reprint-forthcoming.pdf … #Bookofwhy

But look at the cost of this "standard". Invited papers tell readers: "Here is a body of work that has been neglected, catching up requires that we waive the "originality" requirement." If it were not for such editorial leadership, my work would be unknown outside CS. #Bookofwhy

I beg to differ. An "invited survey" has a totally different status than ordinary submission. The requirements of innovation and importance are waived, b/c the editor decides on the latter. There remains only the requirement of "representation." #Bookofwhy

Don't give up so easily. "Ein Lecha Adam SheEin Lo Shaa" says the Mishna (Every person has untapped potential). Even "y=mx+b" is not trivial. I once asked a statistician what Y_x is, when b is correlated with x. Repeating this story would get me into another trouble. #Bookofwhy