"O'Neil's point is that even a frequentist model would have been effective had the data not been massaged."

This post really has nothing to do with techniques or "bad data" or a bayesian vs. frequentist debate.

Her point is that bad modeling was entirely incidental and inconsequential as a cause of the financial meltdown. To repeat an example I used previously, if I smashed your head in with a hammer, Silver would essentially be saying "a hammer caused davesims death."

The statement may be technically true, but it's a shallow analysis of cause and effect. It's unsatisfying because it gives too much significance to an incidental link in the complete chain of events.

> This post really has nothing to do with techniques or "bad data" or a bayesian vs. frequentist debate.

I disagree, but maybe I'm missing something. This early quote, regarding 'bayesian vs. frequentist' seems to sum up O'Neil's view of Silver fairly well to me:

"What is not reasonable, however, is for Silver to claim to understand how the financial crisis was a result of a few inaccurate models, and how medical research need only switch from being frequentist to being Bayesian to become more accurate."

And later on regarding 'bad data,' a point she reiterates several times:

"In other words, it’s not that there are bad statistical approaches which lead to vastly over-reported statistically significant results and published papers (which could just as easily happen if the researchers were employing Bayesian techniques, by the way). It’s that there’s massive incentive to claim statistically significant findings, and not much push-back when that’s done erroneously, so the field never self-examines and improves their methodology. The bad models are a consequence of misaligned incentives."

I do think in the above quote O'Neil is equivocating between the idea of a 'bad model' and the skewed data or overreaching significance applied to the model.

Her fundamental point, which she could have done a better job communicating I think, is that it wasn't the models themselves that were bad, bayesian or otherwise, but the data in the models and at times the inordinate significance applied to those models that was the real cause of the meltdown.

These are particulars to the examples she provided. The unifying idea is that people often choose models or data based on self interest, even at the expense of accuracy. When discussing the cause of an event, it's not enough to say "a bad model is to blame" or "bad data is to blame." At this point, the model or data are instruments serving the self interest of the modeller, and they are incidental in the cause and effect relationship.

That's a good summary of the point I think O'Neil was trying to make contra Silver.

>On this view O'Neil's view is correct: the cause was not "bad modelling" but "bad data." The "bad data" in the model was an "effect" of corruption, not the root "cause" of the financial meltdown.

So it goes that corruption caused bad data, which caused a bad analysis by the model(she claims that both the data and model are bad), which caused the financial meltdown.

That does not invalidate Silver's point. It merely points out that Silver's analysis may be inadequate.

Silver's point can be technically valid, but ultimately shallow and irrelevant. That seems to be what she's arguing.

What I understand is that she means that corruption lead to the financial crisis. The bad models/data were just a smoke screen, or an instrument to get rich quicker.