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I'm not discounting her complaints, but the following is not a confusion of cause and effect: "Silver confuses cause and effect. We didn’t have a financial cri

I'm not discounting her complaints, but the following is not a confusion of cause and effect: "Silver confuses cause and effect. We didn’t have a financial crisis because of a bad model or a few bad models. We had bad models because of a corrupt and criminally fraudulent financial system." A= Financial Crisis B= Bad Models C= Fraudulent System. Nate said "A<-B" Author says "A<-B<-C" That is not a mix up of cause and effect. Author's main complaint seems to be that Nate assumes bad models are an accident, and Author claims they were intentional. Again, not a mixup of cause and effect. At worst it's a naive interpretation of the correct cause.

sillysaurus 14y ago
The bad models were a consequence of a fraudulent system. So by definition, the models weren't the cause of the financial crisis. Therefore the bad models were an effect, in the same way the financial crisis was an effect. If this is true, then the author's claim (mix-up of cause and effect) must be correct.
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- Alex3917 14y ago
  The easiest way to understand this is that even if the models were accurate, the financial crisis would still have occurred. So blaming the models would be confusing cause and effect regardless of whether you think the models were wrong intentionally or unintentionally. That said, I didn't read the book so I have nothing to say about whether or not Nate Silver actually does this.
  - cube13 14y ago
    In order for this to be true, she needed to make the argument that it is. She's not saying anything to that effect at all.
    - sillysaurus 14y ago
      If her point wasn't "corruption caused the financial meltdown," then why did she write this piece? The simplest explanation is that she was, in fact, arguing that corruption caused the financial crisis.
  - mbucc 14y ago
    The models certainly contributed. (Note: I have read this far in the book.) The modeling error in question was independence; that is, if you have five mortgages, each with a 5% change of default, then these can be packaged up as an AAA security as follows: you only lose your money if all five default. A bit riskier package is that you lose if 4/5 default. And so on, each with different returns. If they are independent, the p(default) = 1/20^5. If they are dependent, it is 1/20. Now multiple mortgage pool size by a 100 or 1,000 or 10,000 (?) and see how far off the estimated risk is. :) Now combine this with a 30-to-1 leverage when buying these "AAA" securities. (This was quite a good problem to work through with my daughter to see what that little "independence" assumption means. :) His main point here was that modeling failures are typically due to out-of-sample conditions; when the housing bubble broke, the markets were might more tightly coupled across the country than the modelers assumed. While they could have seen this kind of dependence if they looked to Japan, there was no such precedence in the US in recent history.
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    - jeltz 14y ago
      This is a beginner mistake which I have a hard time believing the entire mortgage market failed to spot. My personal belief is that they knew of the flaw of the models but did not care since their personal incentives were more profitable if the model was not fixed.
      - hindsightbias 14y ago
        As Michael Lewis found in the Big Short. His Liar's Poker (from the 80's) explains the mentality. As he says in the former, he thought the latter would be the be-all end-all. But the kids in Business School would constantly write him and ask him for new ways to rig the game.
- cube13 14y ago
  But it's not. She states: >Rather, the entire industry crucially depended on the false models. Indeed they changed the data to conform with the models, which is to say it was an intentional combination of using flawed models and using irrelevant historical data (see points 64-69 here for more). So C(corruption) directly leads to B(bad model). She does not argue that A(everything falling apart)<-C anywhere in the piece. Silver is claiming that A<-B. He is not commenting about C. So while Silver's claim may not identify the absolute root cause, the author does not actually prove that his cause and effect analysis is flawed. If anything, she has shown that it may be incomplete.
  - sillysaurus 14y ago
    But it's not [true]. What, specifically, isn't true? So C(corruption) directly leads to B(bad model). She does not argue that A(everything falling apart)<-C anywhere in the piece. The point of the piece is that the financial meltdown stemmed from corruption, not from models. If this wasn't her point, then why else would she have written this piece?
    - cube13 14y ago
      She does not prove that claim, though. She argues that corruption causes bad models. For Silver's cause and effect analysis to be incorrect, she needed to prove that the inaccurate models had absolutely nothing to do with the financial meltdown.
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      - btilly 14y ago
        The models can be part of the meltdown without really being a cause. If I choose to shoot you with a gun, and have a variety of appropriate guns, it is not the fault of the specific gun chosen that it was used to kill you. I had motive, opportunity, and alternate means available.
- chc 14y ago
  You're imposing a cause/effect dichotomy that doesn't actually exist. Most causes are effects of something else. When we talk about mixing up cause and effect, we mean that A is said to have caused B when in actuality A was caused by B. Showing that A was caused by C does not mean that we've mixed up cause and effect, because nobody said A caused C.
- hsitz 14y ago
  "The bad models were a consequence of a fraudulent system. So by definition, the models weren't the cause of the financial crisis. Therefore the bad models were an effect, in the same way the financial crisis was an effect." This is faulty reasoning. A pool ball may go into a pocket because it was hit by a cue ball. In this case the cue ball hitting the other ball is the cause of the other ball falling into the pocket. Yes, you can look back into the chain of causes to find one further back, e.g., the fact that a person used a pool cue to hit the cue ball into the other ball. This doesn't mean that the first cause you found was not a cause, and certainly doesn't mean that "by definition". If a corrupt and fraudulent system could have brought about the financial crisis by some means other than causing bad models (which I assume is true), then you can say that the bad models were not a necessary part of the causal chain. But you can't say that they weren't part of the causal chain at all. As a matter of fact (at least according to assumptions everyone is making in this topic's thread) they were.
  - tomkarlo 14y ago
    I'm not sure you can get to the financial crisis without faulty models. Risk models, especially on packaged securities, are crucial to setting ratings as a proxy for risk. Those ratings are key to determining pricing, maximum leverage, and which portfolios can buy them. If the models had been more accurate - for example, if they had more properly reflected the way that the value of a bundle of mortgages will suddenly lose value as housing prices decline - banks would not have been able to pile up such huge risks and high leverage. You can only justify things like 30X leverage if you believe the securities in question have essentially no risk - because at that leverage, a 3% loss in value means you're wiped out.
    - pdonis 14y ago
      I'm not sure you can get to the financial crisis without faulty models. Sure you can. The root cause of the crisis was people gaming the system. There are many ways to game the system; faulty models are only one of them.
      - tomkarlo 14y ago
aptwebapps 14y ago
Exactly. And she finds this mistake so dire she claims it is "malicious" without backing that up at all. Perhaps that was just hyperbole.
kami8845 14y ago
She's actually saying that A<-C AND B<-C implying that B wasn't necessary for A as long as C.
- DigitalJack 14y ago
  Even so, still not a mix up of cause and effect. Wrong cause in this case.
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- jivatmanx 14y ago
  I agree. Also, I'll address the accusation that Nate is confusing cause and effect. Nate's assertion is A<-B If he had confused cause and effect than that corrected form would be B<-A, or, that the financial crisis caused bad models. So the Author's mistake is that (A<-C AND B<-C) != (B<-A), or, that her attack on Nate is inconsistent with what she believes is the correct logic.
  - pdonis 14y ago
    The author isn't saying that A<-B is a confusion of cause and effect. She is saying that B is itself an effect of C, not a cause. (A better way to say it would be "B is not a root cause".)
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sethist 14y ago
I don't think the author is proposing an "A<-B<-C" system. She is stating that the models are really blameless in the situation when the information provided to the models are wrong. The cause and correlation issue arises there. Silver assumes that the systems fail because the models are bad. O'Neil is instead claiming those are just correlations and not a cause and effect relationship. Basically the models are bad and the systems failed because the people providing the data were corrupt. Using your example: "A<-C" and "B<-C"
antoko 14y ago
I don't think the title is her thesis, possibly just link-bait for her usual readership - This seems to be her thesis: To be crystal clear: my big complaint about Silver is naivete, and to a lesser extent, authority-worship.
- Ntrails 14y ago
  linkbait from a website called mathbabe.org? Surely you don't mean that?
  - Maro 14y ago
    Downvoted. Having a domain name that is short and rememberable != linkbait. Surely none of her audience expect to see pics of babes on mathbabe.com
j2labs 14y ago
It's actually both. Bad models and bad incentives, from black swans and the models that ignored them to corrupt practices and liars.
davesims 14y ago
There's some equivocation here I think: "Bad Model" in Silver's view is really "bad modelling" -- a problem with the statistical technique (frequentist vs. bayesian) used to model the data. "Bad Model" in O'Neil's view is really "bad data" -- intentionally skewed numbers as a consequent of conscious or sub-conscious eliding or redactory effects on the data populating the model, for purposes other than accuracy: financial gain, for instance. O'Neil's point is that even a frequentist model would have been effective had the data not been massaged. 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.
- cube13 14y ago
  >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.
  - pygy_ 14y ago
    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.
  - freyr 14y ago
    Silver's point can be technically valid, but ultimately shallow and irrelevant. That seems to be what she's arguing.
- freyr 14y ago
  "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.
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shawn-butler 14y ago
It's known as proximate and ultimate causation. [0] An Aristotelian analysis would say the fraudulent system or its corruption was the final cause, the models were the formal cause. [0] http://en.wikipedia.org/wiki/Proximate_and_ultimate_causation http://en.wikipedia.org/wiki/Proximate_and_ultimate_causatio...

sillysaurus 14y ago

The bad models were a consequence of a fraudulent system. So by definition, the models weren't the cause of the financial crisis. Therefore the bad models were an effect, in the same way the financial crisis was an effect. If this is true, then the author's claim (mix-up of cause and effect) must be correct.

deleted 14y ago
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Alex3917 14y ago
The easiest way to understand this is that even if the models were accurate, the financial crisis would still have occurred. So blaming the models would be confusing cause and effect regardless of whether you think the models were wrong intentionally or unintentionally. That said, I didn't read the book so I have nothing to say about whether or not Nate Silver actually does this.
- cube13 14y ago
  In order for this to be true, she needed to make the argument that it is. She's not saying anything to that effect at all.
  - sillysaurus 14y ago
    If her point wasn't "corruption caused the financial meltdown," then why did she write this piece? The simplest explanation is that she was, in fact, arguing that corruption caused the financial crisis.
- mbucc 14y ago
  The models certainly contributed. (Note: I have read this far in the book.) The modeling error in question was independence; that is, if you have five mortgages, each with a 5% change of default, then these can be packaged up as an AAA security as follows: you only lose your money if all five default. A bit riskier package is that you lose if 4/5 default. And so on, each with different returns. If they are independent, the p(default) = 1/20^5. If they are dependent, it is 1/20. Now multiple mortgage pool size by a 100 or 1,000 or 10,000 (?) and see how far off the estimated risk is. :) Now combine this with a 30-to-1 leverage when buying these "AAA" securities. (This was quite a good problem to work through with my daughter to see what that little "independence" assumption means. :) His main point here was that modeling failures are typically due to out-of-sample conditions; when the housing bubble broke, the markets were might more tightly coupled across the country than the modelers assumed. While they could have seen this kind of dependence if they looked to Japan, there was no such precedence in the US in recent history.
  - deleted 14y ago
    [deleted]
  - jeltz 14y ago
    This is a beginner mistake which I have a hard time believing the entire mortgage market failed to spot. My personal belief is that they knew of the flaw of the models but did not care since their personal incentives were more profitable if the model was not fixed.
    - hindsightbias 14y ago
      As Michael Lewis found in the Big Short. His Liar's Poker (from the 80's) explains the mentality. As he says in the former, he thought the latter would be the be-all end-all. But the kids in Business School would constantly write him and ask him for new ways to rig the game.
cube13 14y ago
But it's not. She states: >Rather, the entire industry crucially depended on the false models. Indeed they changed the data to conform with the models, which is to say it was an intentional combination of using flawed models and using irrelevant historical data (see points 64-69 here for more). So C(corruption) directly leads to B(bad model). She does not argue that A(everything falling apart)<-C anywhere in the piece. Silver is claiming that A<-B. He is not commenting about C. So while Silver's claim may not identify the absolute root cause, the author does not actually prove that his cause and effect analysis is flawed. If anything, she has shown that it may be incomplete.
- sillysaurus 14y ago
  But it's not [true]. What, specifically, isn't true? So C(corruption) directly leads to B(bad model). She does not argue that A(everything falling apart)<-C anywhere in the piece. The point of the piece is that the financial meltdown stemmed from corruption, not from models. If this wasn't her point, then why else would she have written this piece?
  - cube13 14y ago
    She does not prove that claim, though. She argues that corruption causes bad models. For Silver's cause and effect analysis to be incorrect, she needed to prove that the inaccurate models had absolutely nothing to do with the financial meltdown.
    - deleted 14y ago
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    - btilly 14y ago
      The models can be part of the meltdown without really being a cause. If I choose to shoot you with a gun, and have a variety of appropriate guns, it is not the fault of the specific gun chosen that it was used to kill you. I had motive, opportunity, and alternate means available.
chc 14y ago
You're imposing a cause/effect dichotomy that doesn't actually exist. Most causes are effects of something else. When we talk about mixing up cause and effect, we mean that A is said to have caused B when in actuality A was caused by B. Showing that A was caused by C does not mean that we've mixed up cause and effect, because nobody said A caused C.
hsitz 14y ago
"The bad models were a consequence of a fraudulent system. So by definition, the models weren't the cause of the financial crisis. Therefore the bad models were an effect, in the same way the financial crisis was an effect." This is faulty reasoning. A pool ball may go into a pocket because it was hit by a cue ball. In this case the cue ball hitting the other ball is the cause of the other ball falling into the pocket. Yes, you can look back into the chain of causes to find one further back, e.g., the fact that a person used a pool cue to hit the cue ball into the other ball. This doesn't mean that the first cause you found was not a cause, and certainly doesn't mean that "by definition". If a corrupt and fraudulent system could have brought about the financial crisis by some means other than causing bad models (which I assume is true), then you can say that the bad models were not a necessary part of the causal chain. But you can't say that they weren't part of the causal chain at all. As a matter of fact (at least according to assumptions everyone is making in this topic's thread) they were.
- tomkarlo 14y ago
  I'm not sure you can get to the financial crisis without faulty models. Risk models, especially on packaged securities, are crucial to setting ratings as a proxy for risk. Those ratings are key to determining pricing, maximum leverage, and which portfolios can buy them. If the models had been more accurate - for example, if they had more properly reflected the way that the value of a bundle of mortgages will suddenly lose value as housing prices decline - banks would not have been able to pile up such huge risks and high leverage. You can only justify things like 30X leverage if you believe the securities in question have essentially no risk - because at that leverage, a 3% loss in value means you're wiped out.
  - pdonis 14y ago
    I'm not sure you can get to the financial crisis without faulty models. Sure you can. The root cause of the crisis was people gaming the system. There are many ways to game the system; faulty models are only one of them.
    - tomkarlo 14y ago

aptwebapps 14y ago

Exactly. And she finds this mistake so dire she claims it is "malicious" without backing that up at all. Perhaps that was just hyperbole.

kami8845 14y ago

She's actually saying that A<-C AND B<-C implying that B wasn't necessary for A as long as C.

DigitalJack 14y ago
Even so, still not a mix up of cause and effect. Wrong cause in this case.
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jivatmanx 14y ago
I agree. Also, I'll address the accusation that Nate is confusing cause and effect. Nate's assertion is A<-B If he had confused cause and effect than that corrected form would be B<-A, or, that the financial crisis caused bad models. So the Author's mistake is that (A<-C AND B<-C) != (B<-A), or, that her attack on Nate is inconsistent with what she believes is the correct logic.
- pdonis 14y ago
  The author isn't saying that A<-B is a confusion of cause and effect. She is saying that B is itself an effect of C, not a cause. (A better way to say it would be "B is not a root cause".)
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sethist 14y ago

I don't think the author is proposing an "A<-B<-C" system. She is stating that the models are really blameless in the situation when the information provided to the models are wrong. The cause and correlation issue arises there. Silver assumes that the systems fail because the models are bad. O'Neil is instead claiming those are just correlations and not a cause and effect relationship. Basically the models are bad and the systems failed because the people providing the data were corrupt. Using your example: "A<-C" and "B<-C"

antoko 14y ago

I don't think the title is her thesis, possibly just link-bait for her usual readership - This seems to be her thesis: To be crystal clear: my big complaint about Silver is naivete, and to a lesser extent, authority-worship.

Ntrails 14y ago
linkbait from a website called mathbabe.org? Surely you don't mean that?
- Maro 14y ago
  Downvoted. Having a domain name that is short and rememberable != linkbait. Surely none of her audience expect to see pics of babes on mathbabe.com

j2labs 14y ago

It's actually both. Bad models and bad incentives, from black swans and the models that ignored them to corrupt practices and liars.

davesims 14y ago

There's some equivocation here I think: "Bad Model" in Silver's view is really "bad modelling" -- a problem with the statistical technique (frequentist vs. bayesian) used to model the data. "Bad Model" in O'Neil's view is really "bad data" -- intentionally skewed numbers as a consequent of conscious or sub-conscious eliding or redactory effects on the data populating the model, for purposes other than accuracy: financial gain, for instance. O'Neil's point is that even a frequentist model would have been effective had the data not been massaged. 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.

cube13 14y ago
>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.
- pygy_ 14y ago
  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.
- freyr 14y ago
  Silver's point can be technically valid, but ultimately shallow and irrelevant. That seems to be what she's arguing.
freyr 14y ago
"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.

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shawn-butler 14y ago

It's known as proximate and ultimate causation. [0] An Aristotelian analysis would say the fraudulent system or its corruption was the final cause, the models were the formal cause. [0] http://en.wikipedia.org/wiki/Proximate_and_ultimate_causation http://en.wikipedia.org/wiki/Proximate_and_ultimate_causatio...