While that may be best with current PB, I think conditional predictions are useful.

If you are only interested in truth values and not the strength of the prediction, then it is logically equivalent, but the number of points you get is not the same. The purpose of a conditional probability is to take a conditional risk. If Romney is nominated, you get a gratuitous point for this prediction. Of course, simply counting predictions is easy to game, which is why we like to indicate the strength of the prediction, as you do with this one on PB. But turning a conditional prediction into an absolute prediction changes its probability and thus its effect on your calibration score. To a certain extent, it amounts to double counting the prediction about the hypothesis.