35:06 THE 7 PILLARS OF CAUSAL WISDOM 47:12 Pillar 1: graphical models for prediction and diagnosis 57:08 Pillar 2: policy analysis deconfounded 1:19:15 Pillar 3: the algorithmization of counterfactuals 1:23:29 Pillar 4? Formulating a problem in three languages 1:36:35 Pillar 5: Transfer learning, external validity, and sample selection bias 1:50:19 Pillar 6: Missing data 1:50:55 Pillar 7: Causal discovery
Does anyone know where I can find the mathematical proof each level need infomation of that level or above for 3-level hierarchy (association, intervention, counterfactuals) of causality
What a shame... this video is barely audible. A topic of this kind requires top notch audio and I am unable to understand all words sometimes even cannot understand the entire sentence.
Terrible sound. Is it beyond MIT capabilities to furnish the speaker with a body microphone.? I have missed half of this very interesting lecture . What a shame!
29:00 - the example doesn't make any sense, since it says people are exercising MORE as they get older. In fact, based on the chart ALL 50 year olds exercise more than ALL 20 year olds. The logic of the eXercise axis is conveniently ignored to prove a point. Not a good example, and hopefully not how you conduct science...
I should mention that I understand Simpson's paradox, I am simply commenting on the specific, contrived, example that does not work. I am not even fully convinced that Cholesterol (the latent variable) might be correlated with age between the ranges of 10 and 50 if all other factors are held constant.
@@michaeltamillow9722 Good point. This diagram is actually on page 212 of the "Book of Why", something has gone wrong with that example. Maybe the 40 and 50 cloud should be shifted to the left? But it's all about projecting a high-dimensional point cloud onto fewer dimensions the wrong way, yielding a meaningless result, here one about the "typical person". (Cholesterol is also probably mostly correlated with sugar uptake IRL, but that's for some other time 🙂) Fun: "Yule-Simpson’s paradox in Galactic Archaeology"
AI will never ‘know ‘ the cause of gravity. Even though Galilean relative motion gives 50/50 odds that the earth approaches the released object: gravity. Cause of gravity: the earth is expanding at 16 feet per second constant acceleration. Common knowledge since 2002: “The Final Theory: Rethinking Our Scientific Legacy “, Mark McCutcheon. Try to keep up.
35:06 THE 7 PILLARS OF CAUSAL WISDOM
47:12 Pillar 1: graphical models for prediction and diagnosis
57:08 Pillar 2: policy analysis deconfounded
1:19:15 Pillar 3: the algorithmization of counterfactuals
1:23:29 Pillar 4? Formulating a problem in three languages
1:36:35 Pillar 5: Transfer learning, external validity, and sample selection bias
1:50:19 Pillar 6: Missing data
1:50:55 Pillar 7: Causal discovery
Does anyone know where I can find the mathematical proof each level need infomation of that level or above for 3-level hierarchy (association, intervention, counterfactuals) of causality
Hi Kalyana, I think you will enjoy this chapter -- causalai.net/r60.pdf .
CausalAI Thank you so much! This is great!
Good Speech
What a shame... this video is barely audible. A topic of this kind requires top notch audio and I am unable to understand all words sometimes even cannot understand the entire sentence.
Terrible sound. Is it beyond MIT capabilities to furnish the speaker with a body microphone.? I have missed half of this very interesting lecture . What a shame!
Yes, every word is important because the subject matter is quite difficult to understand
God that question session was painful to watch.
Can barely understand a word! It is borderline rude to be so nonchalant about pronunciation and clarity of speech.
Is it me, or he got the best porn-name ever? Anyways, it is a great talk
bruh.
he's got a point though
29:00 - the example doesn't make any sense, since it says people are exercising MORE as they get older. In fact, based on the chart ALL 50 year olds exercise more than ALL 20 year olds. The logic of the eXercise axis is conveniently ignored to prove a point. Not a good example, and hopefully not how you conduct science...
I should mention that I understand Simpson's paradox, I am simply commenting on the specific, contrived, example that does not work. I am not even fully convinced that Cholesterol (the latent variable) might be correlated with age between the ranges of 10 and 50 if all other factors are held constant.
@@michaeltamillow9722 Good point. This diagram is actually on page 212 of the "Book of Why", something has gone wrong with that example. Maybe the 40 and 50 cloud should be shifted to the left? But it's all about projecting a high-dimensional point cloud onto fewer dimensions the wrong way, yielding a meaningless result, here one about the "typical person". (Cholesterol is also probably mostly correlated with sugar uptake IRL, but that's for some other time 🙂) Fun: "Yule-Simpson’s paradox in Galactic Archaeology"
AI will never ‘know ‘ the cause of gravity. Even though Galilean relative motion gives 50/50 odds that the earth approaches the released object: gravity. Cause of gravity: the earth is expanding at 16 feet per second constant acceleration. Common knowledge since 2002: “The Final Theory: Rethinking Our Scientific Legacy “, Mark McCutcheon. Try to keep up.
Your point being…?