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They may be trolling, but it’s a pretty good example of statistical naïveté. What I mean by that is ignoring conditional independence even when it is obvious fr
by jl2718 7y ago
They may be trolling, but it’s a pretty good example of statistical naïveté. What I mean by that is ignoring conditional independence even when it is obvious from common sense. Common sense tells us that living in an old house does not ‘cause’ car accidents, but it also tells us that a poor working elderly person may have a higher risk of both car accidents, and living in an old house. The dependence between the effects disappears when conditioned upon observations closer to the chain of causality.
- baroffoos 7y agoThis also makes me wonder if they house had any relevance at all other than a publicly available show of approximate income. If the researchers had access to all of the incomes of the people they researched would they have been able to build the exact same predictions or were more factors involved like rural/inner city drivers having different results.
- jl2718 7y agoThat's kind of my point. Conditioned upon the right set of variables, they could have probably come up with the opposite 'prediction'.
- asdfasgasdgasdg 7y agoFinish reading the abstract and you'll see there's nothing naive about their analysis.
- akdor1154 7y agoThey don't claim the effect is independent at all, they claim that an insurance company can decide to not bother collecting correct data about the actual causative variables, and instead just base their model off a picture of your house on GSV.
- zo1 7y agoAll good and well, but as far as I know, certain laws are in place that prevent insurance companies from actually using indicators that are "closer to the chain of causality". E.g. poor, elderly (an now recently, male/female). It's then no surprise that they're then actually trying to find other means of determining risk factors for individuals.