Simple explanation of Neural Networks
I have a confession. For a long time now I have found myself befuddled by those who claim to have identified the causes behind observed outcomes in social research via the quantitative analysis of (relatively) large datasets (see posts here, here, and here). For a while, I thought I was seeing the all-to-common confusion of correlation and causation…except that a lot of smart, talented people seemed to be confusing correlation with causation. This struck me as unlikely.
Then, the other day in seminar (I was covering for a colleague in our department’s “Contemporary Approaches to Geography” graduate seminar, discussing the long history of environmental determinism within and beyond the discipline), I found myself in a similar discussion related to explanation…and I think I figured out what has been going on. The remote sensing and GIS students in the course, all of whom are extraordinarily well-trained in quantitative methods, got to thinking about how to determine if, in fact, the environment was “causing” a particular behavior*. In the course of this discussion, I realized that what they meant by “cause” was simple (I will now oversimplify): when you can rule out/control for the influence of all other possible factors, you can say that factor X caused event Y to happen. Indeed, this does establish a causal link. So, I finally get what everyone was saying when they said that, via well-constructed regressions, etc., one can establish causality.
Source: Open The Echo Chamber
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