Model-based analysis lost the big data race, because it's easier to apply stats to data than it is to model underlying causal behaviors
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Replying to @EmilyGorcenski
The consequence of this is that we have fundamentally basic models that adapt only to what features we, with limit understanding, point to.
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Replying to @EmilyGorcenski
This is in some sense fair; in many cases, we don't have the requisite mathematics to fully explain the behaviors we observe.
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Replying to @EmilyGorcenski
But it is terrifying when we elide their existence entirely. Instead, we use simplified techniques to understand very complex phenomena...
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Replying to @EmilyGorcenski
and then use these limited conclusions to make actual policy decisions. In the worst case, these conclusions determine who to drone-strike.
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Replying to @EmilyGorcenski
But we chug ahead because computing power is cheap, databases are ultimately pretty easy, and the ramifications ultimately seem distant.
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Replying to @EmilyGorcenski
This is not to say that data science is irrelevant. But there is a missing component and that is a posteriori validation of models...
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Replying to @EmilyGorcenski
along with an explicit process in which to do so. Data analysis is a step in a solution process, not a solution in and of itself.
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We too often confuse provability in a mathematical context with truth in the broad strokes of human experience.
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