Their conversation begins with a discussion of "recidivism risk scores," which judges often use in determining sentencing for convicted felons. Roberts and O'Neil are similarly disturbed by this use of data...and they reopen a common EconTalk theme of late, the distinction between accuracy and causality. (In another recent episode, Susan Athey referred to this distinction as that between prediction and causation.) The conversation moves to the issue of merit pay for teachers and commercial applications of big data. As you can imagine, Roberts's and O'Neil's concerns begin to diverge. The big question I'm left with is whether to be optimistic or pessimistic about the applications of Big Data moving forward...Both Roberts and O'Neil agree we're still in its early days...What do you think? Does Big Data do more harm than good?