> "I’ve never been able to understand Pearl’s notation:" -- Gelman. That should not be surprising: "It is difficult to get a man to understand something when

> "I’ve never been able to understand Pearl’s notation:" -- Gelman. That should not be surprising: "It is difficult to get a man to understand something when his salary depends upon his not understanding it." -- Upton Sinclair Accepting Pearl would amount to stating that some of the procedures we(statisticians) have been using, championing and sourcing funds for, for half a century are seriously flawed. Thats going to have consequences on future funding. Of course there will be resistance. Tony Hoare is worth paraphrasing -- some methods are so crisp and small that they are obviously correct. Others are so complex that one cannot find obvious errors. Piling on a hierarchy of random variables upon random variables and parameters upon prameters lies firmly in the latter class. This is actually a charitable analogy because some uses of statistical methods are incorrect but the error lies in incorrect use -- Using a tool or a technique to answer a question that it cannot answer. Smothering it with complexity and phrases like 'but real world', 'but noisy big data' helps to muddy the waters enough to deflect the attention from the fundamentals difference beween conditioning and intervening. I can be sympathetic to a claim that a method is more effective solving a complicated problem than a simple one. On the otherhand, if it turns out that the body of theory on which the proposed method has been built, the same proposed method that is presumably correct for the complicated case, cannot deal with a pedagogic toy scenario correctly, that raises my eyebrows.