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"
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"