Intuitively, (38a) is true, as are \(\mathsf{\neg A &gt;\neg L}\) and \(\mathsf{\neg B &gt;\neg L}\), but (38b) is false. Champollion, Ciardelli, and Zhang (2016: 321) argue that a similarity analysis cannot predict \(\mathsf{\neg A &gt;\neg L}\) and \(\mathsf{\neg B &gt;\neg L}\) to be true, while (38b) is false. In order for \(\mathsf{\neg A &gt;\neg L}\) to be true, the particular fact that Switch B is up must count towards similarity. Similarly, for \(\mathsf{\neg B &gt;\neg L}\) to be true, the particular fact that Switch A is up must count towards similarity. But then it follows that (38b) is true on a similarity analysis: the most similar worlds where A and B are not both up have to either be worlds where Switch B is down but Switch A is still up, or Switch A is down and Switch B is still up. In those worlds, the light would be off, so the similarity analysis incorrectly predicts (38b) to be true. Champollion, Ciardelli, and Zhang (2016) instead pursue a semantics in terms of causal models where counterfactually making \(\neg \mathsf{(A\land B)}\) true and making \(\mathsf{\neg A\lor\neg B}\) true come apart.