Re: [Vo]:my blog associate's new paper
In the mode of addressing the relevance of the trope correlation doesn't imply causation to the issue of governments testing social theories on unwilling human subjects: About 15 years ago, I was working at a well-funded Silicon Valley startup in Palo Alto with about 100 people. During the few years I was there, 5 children of parents working there were diagnosed on the autism spectrum. I contacted the Berkeley epidemiologist who had been studying autism and informed him of the anomaly. His response was simply that We know such clusters exist in Silicon Valley and we don't know what causes them. Well, DUH! I was outraged. Several years later I was able to get data out of the Dept of Education on the incidence of autism by State. I could not locate data by county. So I did what any _reasonable epidemiologist should_ do with such data: I surveyed the list of current hypotheses of causes of autism, added a few, and gathered State-level data on other variables related to those hypotheses to look for *gasp* CORRELATIONS. Now, none of this would be in the _least_ controversial, except that one of the hypotheses was that the recent increase in immigration from India to places like Silicon Valley was bringing in a pathogen -- possibly intestinal -- being spread in some manner such as restaurants. Moreover, the project wouldn't have been controversial even then because if you look at the rank-order of single-variable correlations, the correlation with immigrants from India doesn't beat mother's age at first live birth (one hypothesis is father's age producing errors in the sperm's DNA -- for which MAAFLB is a proxy). However, if we're looking at a population with high susceptibility -- say genetic background from human ecologies with low population densities -- then you have to construct a composite variable as the conjunction between the susceptible population and the vector population. L Lo and behold, when all 2-variable conjunctions were correlated with autism incidence, the pair that came out on top was immigrants from India per capita and Finnish ancestry per capita. NOW we're in serious trouble for oblivious political reasons! So I added hundreds more demographic variables to see if, by chance, I could get some pairs of variables to beat that pair -- not that this would, by itself, invalidate the correlation; such scatter-shot searches for correlations are notorious as a statistical fallacy called data-mining in which you have no idea of what class of correlations you're looking for and, just by pure chance, you can expect to find some ranking higher so you can't automatically conclude they are significant even though the Pearson's 'r' and degrees of freedom (sample size) -- taken out of the data-mining context -- might indicate high significance. What I found was that, indeed, there were higher correlation pairs but in the scatter plot for the correlation in question, there were some data points that seemed as particular statistical outliers. This is a common problem in science and it can result from a large number of things -- but usually some kind of measurement error. It is standard procedure, in such scenarios, to throw out the top and bottom measurements -- thereby reducing the sample size but hopefully ending up with a higher quality sample. Doing that, the India immigrant x Finnish ancestry pair once again topped the list which now included a combinatorial explosion of pairs. So we're still far from out of the scientific woods (let alone political woods) with this since there the single variable correlation with mother's age at first live birth is nipping at the heels of the politically volatile correlation. Moreover, the MAAFLB scatter plot is more 'normal' or 'robust', meaning that the data points spread out relatively evenly around the regression line, whereas the politically volatile correlation is ragged -- far from 'normal'. You can try to discount the ragged correlation scatter and keep the high rank for the politically volatile correlation by invoking confounding variables such as differing standards of autism diagnosis applied across different states, etc. However, the fact remains that the MAAFLB correlation is less complicated (single variable) and is more robust. OK, so where does this leave us? Well, if I were forced to choose one hypothesis as a working hypothesis I'd say father's age is the correct hypothesis -- not because it avoids the nasty politics of immigration -- but simply on standard statistical merits. However, life isn't so kind to us as to allow us to ignore all alternative hypotheses -- even when those hypotheses might be considered Hate Data. This is particularly true when you have something as devastating to families, already struggling with the disappearance of middle class jobs, as autism mysteriously exploding in incidence. But it gets worse: Once I had this database of hundreds of by-State demographic variables, I decided to -- just
[Vo]:my blog associate's new paper
I recommend to all my friends to read http://egooutpeters.blogspot.co.at/2013/10/ways-6-teacher.html It is a paper from a series authored by my Blog associate Georgina Popescu. Gina is a very wise finantist- as Nassim Nicholas Taleb. I think this formulation:**I do not believe that we will solve our moral dilemmas by splitting the wor* **ld into 99% vs. 1% - the poor and pure vs. the rich and rotten. *has to become a meme-like quotation. Finance, it seems is an excellent school of complexity. The series 'Ways is about the Crisis- a problem solving approach;the Crisis is actually a by bunch of intertwined wicked or even intractable problems that can be solved only by applying Rule 18- change the premises. The same is true for LENR. Peter -- Dr. Peter Gluck Cluj, Romania http://egooutpeters.blogspot.com
Re: [Vo]:my blog associate's new paper
It is supremacist for the government to test its social theories on unwilling human subjects. It doesn't matter how much evidence one can bring to bear in sociological journals, let alone pundit pieces in the fashion press of the intelligentsia, in support of this or that social theory; imposing them on unwilling human subjects violates humanity. Quite aside from the fact that correlation doesn't imply causation, thereby rendering any mountain of data-collection incapable of scientific proof of causality in the social sciences, it is more compassionate to let people learn live out their strongly held beliefs and thereby learn from their mistakes then it is to engender their unquenchable hatred. On Fri, Oct 4, 2013 at 2:18 AM, Peter Gluck peter.gl...@gmail.com wrote: I recommend to all my friends to read http://egooutpeters.blogspot.co.at/2013/10/ways-6-teacher.html It is a paper from a series authored by my Blog associate Georgina Popescu. Gina is a very wise finantist- as Nassim Nicholas Taleb. I think this formulation:**I do not believe that we will solve our moral dilemmas by splitting the wor* **ld into 99% vs. 1% - the poor and pure vs. the rich and rotten. *has to become a meme-like quotation. Finance, it seems is an excellent school of complexity. The series 'Ways is about the Crisis- a problem solving approach;the Crisis is actually a by bunch of intertwined wicked or even intractable problems that can be solved only by applying Rule 18- change the premises. The same is true for LENR. Peter -- Dr. Peter Gluck Cluj, Romania http://egooutpeters.blogspot.com
Re: [Vo]:my blog associate's new paper
James Bowery jabow...@gmail.com wrote: Quite aside from the fact that correlation doesn't imply causation, Actually it does, as David Hume pointed out. In natural science, that is pretty much all you have to go on in many cases. - Jed
RE: [Vo]:my blog associate's new paper
From: Jed Rothwell James Bowery wrote: Quite aside from the fact that correlation doesn't imply causation, Actually it does, as David Hume pointed out. In natural science, that is pretty much all you have to go on in many cases. This is an interesting point which includes LENR in several ways in the sense of thermal gain being correlated with a nuclear reaction. In fact, no one is correct and no one is wrong when we talk about timing and coincidence. Turns out ... wiki-the-wonderful has an actual entry entitled Correlation does not imply causation which is a phrase used in statistics, which was purposely designed to over-emphasize the point that a correlation in time between two variables does not necessarily (or always) imply that one causes the other. Unfortunately this phrase is statistically false in itself - in that much of the time there is indeed this exact kind of temporal connection. Therefore, as Jed suggests, more often than not - correlation does imply causation. But the intent of phrasing of this bit of logic, having being cast in the negative - is to avoid the generalization fallacy... which is very similar to the racism fallacy. Also in law, we have what is known as proximate cause, which is a recognition that timing can be important, but there is also the recognition that coincidence can invariably influence proper judgment of causation - in a negative sense. Many statistical tests calculate correlation between variables. A few go further and calculate the likelihood of a true causal relationship; examples are the Granger causality test and convergent cross-mapping. The counter assumption, that correlation proves causation, is considered to be a questionable cause logical fallacy in that two events occurring together are taken to have a cause-and-effect relationship. This fallacy is also known as cum hoc ergo propter hoc, Latin for with this, therefore because of this. A similar fallacy: that an event that follows another was necessarily a consequence of the first event, is sometimes described as post hoc ergo propter hoc (Latin for after this, therefore because of this). IOW - in a few instances, a recurring correlation of the parameter of time will falsely imply causation; and recurring coincidence is a valid counter-argument to this implication... yet often, timing especially when repeatable, does indeed influence causation in a fundamental sense. Therefore, the bottom line is that there can be repercussions which are best avoided by foregoing this correlation/causation assumption - just as we forego assumptions deriving from race, gender and so on... In science we should seek real proof of causation - and correlation in time is NOT real proof... even when it can suffice as good evidence most of the time. Jones attachment: winmail.dat
