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> Having a Hotmail account is a symptom of a problem which could lead to higher risk, but it is judging that symptom as if it is a cause which leads to risk. N

> Having a Hotmail account is a symptom of a problem which could lead to higher risk, but it is judging that symptom as if it is a cause which leads to risk. No, it's not judging that symptom as a cause. It doesn't even matter if it's a cause or a symptom, as long as it is statistically sound. Hotmail, like [not] smoking, [not] exercising, [not] drinking alcohol, is a choice. Having chosen one way or the other does, statistically, imply other things. Whether we, as a society, want to allow this kind of statistical inference to meaningfully impact things like insurance costs, prison terms and other important decisions is a completely different issue.

kome 9y ago
gosh. no, having a hotmail account correlation it doesn't imply anything at all. is not causation, that's worth repeating 1000 times. > Hotmail, like [not] smoking, [not] exercising, [not] drinking alcohol, is a choice. lol.
- fpig 9y ago
  Yelling that correlation is not causation 1000 times is ignorant when the effectiveness of a method depends only on correlation, and does not depend on the existence of a causal link. And in the case of filtering, that is the case. Think screening for a virus vs trying to cure the virus. If warts are a symptom of the virus, having warts absolutely matters in screening. However, cutting off the warts will not remove the virus.
- beagle3 9y ago
  > correlation it doesn't imply anything at all. is not causation, that's worth repeating 1000 times. You might want to understand correlation, causation and dependence, and implication before repeating that 1000 times. I specifically used the term "statistically imply", which is correct, and means "has predictive power", which is true in this case of correlation whether or not causation is involved. Correlation actually implies a lot of things. At the very least, it implies statistical dependence (to the confidence attainable with the data). The converse is NOT true -- lack of correlation does not imply independence. But as independence implies LACK of correlation, correlation necessarily implies dependence. This is very basic probability, and is relevant to estimation of which insurance is an application. Causation is a completely different subject. > lol Care to elaborate? What exactly do you disagree with? Is any of the listed things not a choice?
fpig 9y ago
That's correct on all counts except it's also irrelevant whether the variable is a choice or not.
- beagle3 9y ago
  It's a philosophical/ethical/legal question what should be allowed to go into insurance rates, so relevance is determined by the framework in which you evaluate things. I am personally ok with higher car insurance premiums for people with a prior DUI conviction, and higher health insurance premiums for smokers; I'm not ok with higher health insurance premiums for albinos, or people with a family history of cardiac problems or family history of type I diabetes -- even though they are mathematically justified (which means that -- not being in either group -- my own premiums are higher as a result).
  - fpig 9y ago
    Yes, but that is a different issue as you said in your original comment. Statistically it is irrelevant. The "is it a choice" part also belongs in the "completely different issue" basket, so to speak.
rrobukef 9y ago
Then it needs to be clear what the effects of the choice are, so you can choose. Then the effect will disappear because it's not a true predictor. Each of your examples have a clear explanation, hotmail doesn't.
- beagle3 9y ago
  What's a "true predictor"? Statistics deals with useful predictors (biased/unbiased, efficient/inefficient, sufficient/insufficient etc), and in that sense, it's likely that a hotmail account is a good predictor of (not) being good or up to date with modern computer technology; that's definitely true of my sample anecdata (and AOL is a stronger predictor of the same). Now, assume for a second that ability to use modern technology (like touch screens and digital displays) correlates with safer driving -- an assumption that could actually be tested. If that's the case, then you would probably accept that a score on some objective[0] test of tech savvy could affect car insurance rates, right? An objective, though slightly less accurate, estimator of tech savvy is the domain name - "@microsoft.com" or "@google.com" means likely savvy, "@aol.com" or "@hotmail.com" likely less so. Age, Gender and Religion are other predictors of the same kind; e.g. observant Jews have significantly lower (~15% in some stats) chance of being in a car accident. That's not because they are better drivers or drive better cars - it's because they refrain from driving for one day a week. Knowing someone is Jewish therefore implies lower chance of being in a car accident - maybe only 3%, because 80% do not observe. So "being Jewish" is, statistically, an informative predictor. Again, I am not saying these SHOULD be legal - but that's a philosophical/legal issue. Where I now reside, the legally approved predictors are: (a) age, (b) accidents in the last 5 years, (c) most recent validity period of drivers license, and (d) age and safety rating of car. It's not as "free" a market, but it is much saner. [0] but objectivity is subjective... http://wiki.c2.com/?ObjectivityIsAnIllusion http://wiki.c2.com/?ObjectivityIsAnIllusion
  - rrobukef 9y ago
    Let's say a true predictor is causal. Because each example you give is causal: age causes defects, smoking causes sickness, alcohol impairs, religion causes behaviour. Using hotmail versus gmail will not cause something -- private domains are outliers. If you make the predictor transparent the effect should disappear because no one will use a hotmail address unless they are technically inept. I know some non-causal predictors, yet they're all controversial: various data as predictors for race, for example an address to determine which part of the city one lives. This is my argument: it could be a `good` predictor but it's a proxy and it only works because there is no transparency. As such I don't think it's ethical for two different reasons. I'd also like to add to your anecdata: I still use my first hotmail email as primary. I'm a young technical and safe driver (I hope).

kome 9y ago

gosh. no, having a hotmail account correlation it doesn't imply anything at all. is not causation, that's worth repeating 1000 times. > Hotmail, like [not] smoking, [not] exercising, [not] drinking alcohol, is a choice. lol.

fpig 9y ago
Yelling that correlation is not causation 1000 times is ignorant when the effectiveness of a method depends only on correlation, and does not depend on the existence of a causal link. And in the case of filtering, that is the case. Think screening for a virus vs trying to cure the virus. If warts are a symptom of the virus, having warts absolutely matters in screening. However, cutting off the warts will not remove the virus.
beagle3 9y ago
> correlation it doesn't imply anything at all. is not causation, that's worth repeating 1000 times. You might want to understand correlation, causation and dependence, and implication before repeating that 1000 times. I specifically used the term "statistically imply", which is correct, and means "has predictive power", which is true in this case of correlation whether or not causation is involved. Correlation actually implies a lot of things. At the very least, it implies statistical dependence (to the confidence attainable with the data). The converse is NOT true -- lack of correlation does not imply independence. But as independence implies LACK of correlation, correlation necessarily implies dependence. This is very basic probability, and is relevant to estimation of which insurance is an application. Causation is a completely different subject. > lol Care to elaborate? What exactly do you disagree with? Is any of the listed things not a choice?

fpig 9y ago

That's correct on all counts except it's also irrelevant whether the variable is a choice or not.

beagle3 9y ago
It's a philosophical/ethical/legal question what should be allowed to go into insurance rates, so relevance is determined by the framework in which you evaluate things. I am personally ok with higher car insurance premiums for people with a prior DUI conviction, and higher health insurance premiums for smokers; I'm not ok with higher health insurance premiums for albinos, or people with a family history of cardiac problems or family history of type I diabetes -- even though they are mathematically justified (which means that -- not being in either group -- my own premiums are higher as a result).
- fpig 9y ago
  Yes, but that is a different issue as you said in your original comment. Statistically it is irrelevant. The "is it a choice" part also belongs in the "completely different issue" basket, so to speak.

rrobukef 9y ago

Then it needs to be clear what the effects of the choice are, so you can choose. Then the effect will disappear because it's not a true predictor. Each of your examples have a clear explanation, hotmail doesn't.

beagle3 9y ago
What's a "true predictor"? Statistics deals with useful predictors (biased/unbiased, efficient/inefficient, sufficient/insufficient etc), and in that sense, it's likely that a hotmail account is a good predictor of (not) being good or up to date with modern computer technology; that's definitely true of my sample anecdata (and AOL is a stronger predictor of the same). Now, assume for a second that ability to use modern technology (like touch screens and digital displays) correlates with safer driving -- an assumption that could actually be tested. If that's the case, then you would probably accept that a score on some objective[0] test of tech savvy could affect car insurance rates, right? An objective, though slightly less accurate, estimator of tech savvy is the domain name - "@microsoft.com" or "@google.com" means likely savvy, "@aol.com" or "@hotmail.com" likely less so. Age, Gender and Religion are other predictors of the same kind; e.g. observant Jews have significantly lower (~15% in some stats) chance of being in a car accident. That's not because they are better drivers or drive better cars - it's because they refrain from driving for one day a week. Knowing someone is Jewish therefore implies lower chance of being in a car accident - maybe only 3%, because 80% do not observe. So "being Jewish" is, statistically, an informative predictor. Again, I am not saying these SHOULD be legal - but that's a philosophical/legal issue. Where I now reside, the legally approved predictors are: (a) age, (b) accidents in the last 5 years, (c) most recent validity period of drivers license, and (d) age and safety rating of car. It's not as "free" a market, but it is much saner. [0] but objectivity is subjective... http://wiki.c2.com/?ObjectivityIsAnIllusion http://wiki.c2.com/?ObjectivityIsAnIllusion
- rrobukef 9y ago
  Let's say a true predictor is causal. Because each example you give is causal: age causes defects, smoking causes sickness, alcohol impairs, religion causes behaviour. Using hotmail versus gmail will not cause something -- private domains are outliers. If you make the predictor transparent the effect should disappear because no one will use a hotmail address unless they are technically inept. I know some non-causal predictors, yet they're all controversial: various data as predictors for race, for example an address to determine which part of the city one lives. This is my argument: it could be a `good` predictor but it's a proxy and it only works because there is no transparency. As such I don't think it's ethical for two different reasons. I'd also like to add to your anecdata: I still use my first hotmail email as primary. I'm a young technical and safe driver (I hope).