Christian, I don’t usually post here anymore, but I am going to reiterate a point I made recently: advocating for a vaccine that isn’t adequately tested is coming close to health fraud.
Testing requirements are fairly onerous, but that is for a good reason.
IlyaShpitser
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Recommending this to others seems to be coming pretty close to health fraud.
The reasonably ponderous systems in place for checking if things work and aren’t too risky are there for a reason.
“That’s the test. Would you put it in your arm rather than do nothing? And if the answer here is no, then, please, show your work.”
Seems to be an odd position to take to shift the burden of proof onto the vaccine taker rather than than the scientist.---
I think a lot of people, you included, are way overconfident on how transmissible B.1.1.7. is.
90% of the work ought to go into figuring out what fairness measure you want and why. Not so easy. Also not really a “math problem.” Most ML papers on fairness just solve math problems.
A whole paper, huh.
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I am contesting the whole Extremely Online Lesswrong Way<tm> of engaging with the world whereby people post a lot and pontificate, rather than spending all day reading actual literature, or doing actual work.
“Unless you’d put someone vulnerable at risk, why are you letting another day of your life go by not living it to its fullest? ”
As soon as you start advocating behavior changes based on associational evidence you leave the path of wisdom.
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You sure seem to have a lot of opinions about statisticians being conservative about making claims without bothering to read up on the relevant history and why this conservativism might have developed in the field.
You can read Halpern’s stuff if you want an axiomatization of something like the responses to the do-operator.
Or you can try to understand the relationship of do() and counterfactual random variables, and try to formulate causality as a missing data problem (whereby a full data distribution on counterfactuals and an observed data distribution on factuals are related via a coarsening process).
How is this different from just a regular imperative programming language with imperative assignment?
Causal models are just programs (with random inputs, and certain other restrictions if you want to be able to represent them as DAGs). The do() operator is just imperative assignment.
Here are directions: https://www.instructables.com/id/The-Pandemic-Ventilator/
I think the sorts of people I want to see this blog website will know what to do with the information on it.
Medical information on covid-19: https://emcrit.org/ibcc/covid19/
https://panvent.blogspot.com/ ← Spread this to your biomedical engineering friends, or any hobbyist who can build things. We need to ramp up ventilator capacity, now. Even if they are 80% as good as a high tech one, but cheap to make, they will save lives.
There’s a long history of designing and making devices like these for the Third World places that need them. We will need these soon, here and everywhere.
Some references to lesswrong, and value alignment there.
anyone going to the AAAI ethics/safety conf?
One of my favorite examples of a smart person being confused about something is ET Jaynes being confused about Bell inequalities.
Smart people are confused all the time, even (perhaps especially) in their area.
You are really confused about statistics and learning, and possibly also about formal languages in theoretical CS. I neither want nor have time to get into this with you, just wanted to point this out for your potential benefit.
http://callingbullshit.org/syllabus.html
(This is not “Yudkowskian Rationality” though.)
Dear Christian, please don’t pull rank on my behalf. I don’t think this is productive to do, and I don’t want to bring anyone else into this.
well, using philosophy i did that hard part and figured out which ones are good.
If there is really both reverse causation and regular causation between Xr and Y, you have a cycle, and you have to explain what the semantics of that cycle are (not a deal breaker, but not so simple to do. For example if you think the cycle really represents mutual causation over time, what you really should do is unroll your causal diagram so it’s a DAG over time, and redo the problem there).
You might be interested in this paper (https://arxiv.org/pdf/1611.09414.pdf) that splits the outcome rather than the treatment (although I don’t really endorse that paper).
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The real question is, why should Xc be unconfounded with Y? In an RCT you get lack of confounding by study design (but then we don’t need to split the treatment at all). But this is not really realistic in general—can you think of some practical examples where you would get lucky in this way?