This feels right to me. I can’t implement it, and I’m not sure I could explain what Eli said, but I understand Pearl well enough (at an intuitive level) to say that it feels like the kind of additions Eli is talking about would clarify and reach the results he’s talking about.
Read Pearl. It’s not mathy, it’s mostly words about graph manipulation.
If you’re bothered by math, read Pearl anyway. He doesn’t use equations or make you transform symbols. If you can think about information flows or reason visually, Pearl’s calculus is for you. You’ll understand what it means for something to be a cause or a possible cause or not a possible cause of something else in a deeper way than you do before Pearl.
If you’re already comfortable with math, there’s nothing hard about the theory, it’s just using a different formalism than linear symbols to explain how events are connected causally.
If it helps, I am happy to help with the technical content of the book, or with general technical questions about causal inference (either over email or here).
That’s “Causality: models, reasoning, and inference By Judea Pearl”...? “Not mathy”? It’s jammed full of dense maths! It has integration symbols, summation symbols, logic, probability, theorems and lemmas coming out of its ears! Obviously, Pearl is showing off to impress his peers ;-)
okay, you’re right they’re in there, but Pearl uses those in the proofs, not the explanations, as I recall. I don’t think you have to understand the proofs to get the idea.
If you find math oppressive, let me know if you try Pearl and find it too daunting. If that happens, I’ll change the way I describe the book, I promise.
This feels right to me. I can’t implement it, and I’m not sure I could explain what Eli said, but I understand Pearl well enough (at an intuitive level) to say that it feels like the kind of additions Eli is talking about would clarify and reach the results he’s talking about.
Read Pearl. It’s not mathy, it’s mostly words about graph manipulation.
If you’re bothered by math, read Pearl anyway. He doesn’t use equations or make you transform symbols. If you can think about information flows or reason visually, Pearl’s calculus is for you. You’ll understand what it means for something to be a cause or a possible cause or not a possible cause of something else in a deeper way than you do before Pearl.
If you’re already comfortable with math, there’s nothing hard about the theory, it’s just using a different formalism than linear symbols to explain how events are connected causally.
Thanks Eli.
Second Chris’ advice on reading Pearl.
If it helps, I am happy to help with the technical content of the book, or with general technical questions about causal inference (either over email or here).
I’ve tried to read Pearl’s decision theory book, but it seemed dry and boring. Guess I’ll have to give it another go...
It’s available online too, but don’t pirate it.
That’s “Causality: models, reasoning, and inference By Judea Pearl”...? “Not mathy”? It’s jammed full of dense maths! It has integration symbols, summation symbols, logic, probability, theorems and lemmas coming out of its ears! Obviously, Pearl is showing off to impress his peers ;-)
okay, you’re right they’re in there, but Pearl uses those in the proofs, not the explanations, as I recall. I don’t think you have to understand the proofs to get the idea.
If you find math oppressive, let me know if you try Pearl and find it too daunting. If that happens, I’ll change the way I describe the book, I promise.
Probably a little, but it does help you find mistakes where they exist.
(Okay, that was showing off.)