> Well, there are some caveats even here. The real gold standard is a double-blind experiment, in which neither the subjects nor the experimenters know who is in which group. In the case of car color, we would literally have to blind the drivers, which would of course raise the accident rate considerably. Not really. It's just enough that we don't tell the red car drivers that they're part of a special group. Just le…
What if being assigned a red car caused drivers to drive more carefully because they believe that red cars attract the attention of law enforcement more so than other colors?
> Well, there are some caveats even here. The real gold standard is a double-blind experiment, in which neither the subjects nor the experimenters know who is in which group. In the case of car color, we would literally have to blind the drivers, which would of course raise the accident rate considerably. Not really. It's just enough that we don't tell the red car drivers that they're part of a special group. Just le…
> Just let them think we have equal number of different colors assigned to different drivers (and don't let them see what the others got).
They, the drivers, shouldn't know anything about the experimental design in the first place. Let alone equal number of different colors.
It point out the cliche, "correlation is not equal to causation."
It is a real thing but throwing that quote around without reading into the research or having proper foundation to assess a conducted research is just as bad.
I am not sure how to define "cause" without introducing counterfactuals. "This happened because I did that" seems to imply "Had I not done that, this would not have happened" -- a counterfactual.
I'm not sure that's always the case. Couldn't you have a scenario where your action was the direct cause of an outcome that might have come about some other way without your involvement at all? For instance, if a ball rolls down a hill because you pushed it, but a breeze blows a moment after you pushed the ball, if that breeze was strong enough to push the ball down the hill too, then you have a scenario where "that…
That seems like an unintentional straw man. The actual counterfactual is: "had I not pushed the ball, it would not have rolled down this hill [at the moment it did]". That says nothing about whether some other cause could not push the ball over at a later instant. Equivalently (if you don't like separating out the causes in time), if formulated as an OR operation on a bunch of causes, the counter factual would be: "Had I not pushed the ball, either the ball would not have rolled down the hill, or there would have been another cause for pushing it down the hill."
Recent appearance on Sam Harris podcast, I quite enjoyed it.... https://samharris.org/podcasts/164-cause-effect/ #164 - Cause & Effect - A Conversation with Judea Pearl August 5, 2019 In this episode of the Making Sense podcast, Sam Harris speaks with Judea Pearl about his work on the mathematics of causality and artificial intelligence. They discuss how science has generally failed to understand causation, different…
I would strongly recommended this podcast.
I really enjoyed hearing Judea Pearl being interviewed, as I am most of the way through "The Book of Why" and have learned a lot from it. I did feel that Sam steered the conversation a bit too much towards his favorite topics (like free will) and wish there was a bit more discussion of philosophy/history of science, but it was still a great listen.
I first learned of Judea Pearl by stumbling across the transcript of a talk he gave while I was researching DAGs: http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm . The way he grounded his talk in the history of thought hooked me, and the talk serves as a good general overview for those deciding if they want to pick up the book.
I’m 30 pages into the book. It hasn’t grabbed me yet. Worth finishing?
The writing style does not change much, and I do find that it's taking me longer than usual to work my way through the book, especially since I'm very interested in the subject matter. That being said, there are some good nuggets of information a little further in. Understanding some of the patters that pop up in the causal scenarios he lays out and being able to think about these with a shorthand or graphically has changed the way I think about complex situations.
I like the review, but the criticism of distinguishing causality from counterfactual reasoning feels weak to me. Do we actually care about the counterfactual reasoning most? Of course. But establishing causality as its own thing before counterfactuals is necessary in the way that Pearl has structured his math. And even the grammar of human languages enforces this separation of concepts. Do you need causality to even…
The key is between hume rejection and data only, there are spectrum of links. Double bind rct ...
Post is sweet. It point out the cliche, "correlation is not equal to causation." It is a real thing but throwing that quote around without reading into the research or having proper foundation to assess a conducted research is just as bad.
Yes, indeed, its also interesting to notice that people struggle when one asks "Why isn't it?" One often gets strange statistical answers that don't really get to the root of anything.