I have a postgraduate degree in statistics, and the more I learn about this material the more I'm impressed with Judea Pearl and his colleagues. Prof. Pearl is also a very impressive human being, even if you disregard his acheivements described here. Look him up: he's awesome.
Who ever put this together, the differences between the slides in some cases are (or at least appear to be) subtle... next time please show the transitions.
You do not need models for causality. If he mentions models like the economics example he gives. Models are helpful in predicting things and not that great on inference. I would recommend looking into Rubin's talk for that aspect. Interesting talk though but that what he proposes is not so strong like what Rubin proposes.. so ..i think under many practical constraints one can use Rubin's approach , if you are using prediction to imply causality it would be like a simulation and emulation approach, you really need to be sure of what you have simulated and emulated. All said and done, am glad he gave this talk and people are concerned about causality.
![Judea Pearl -- The Foundations of Causal Inference [The Book of WHY]](/img/1.gif)
I have a postgraduate degree in statistics, and the more I learn about this material the more I'm impressed with Judea Pearl and his colleagues. Prof. Pearl is also a very impressive human being, even if you disregard his acheivements described here. Look him up: he's awesome.
Thank you. It would help if we saw more of the slides when Judea is talking.
This lecture here is a bloody masterpiece
Slides: simons.berkeley.edu/sites/default/files/docs/422/pearljudea.pdf
360p? WHY?
This comment probably made me laugh a bit too much.
Because it's from Microsoft, the company that treats the end-user with disdain, Like most companies, to be fair; they're just more blatant.
So that someone can come along to practice using super-resolution imaging. ;)
Who ever put this together, the differences between the slides in some cases are (or at least appear to be) subtle... next time please show the transitions.
Causal Inference in Statistics: A Primer brought me here
See _Leap Of Logic_ by physicist David Harriman for induction.
You do not need models for causality. If he mentions models like the economics example he gives. Models are helpful in predicting things and not that great on inference. I would recommend looking into Rubin's talk for that aspect. Interesting talk though but that what he proposes is not so strong like what Rubin proposes.. so ..i think under many practical constraints one can use Rubin's approach , if you are using prediction to imply causality it would be like a simulation and emulation approach, you really need to be sure of what you have simulated and emulated. All said and done, am glad he gave this talk and people are concerned about causality.
I hope you understand what you just said.