 One issue concerns the role of causal information in scientific explanation. All of the traditional models considered above attempt to capture causal explanations, although some attempt to capture non-causal explanations as well. It is a natural thought (endorsed by many) that many of the difficulties faced by the models described above derive at least in part from their reliance on inadequate treatments of causation.[15]  The problems of explanatory asymmetries and explanatory irrelevance described in  Section 2.5  seem to show that the holding of a law between C and E is not sufficient for C to cause E; hence not a sufficient condition for C to figure in an explanation of E. If the argument of  section 3.3  is correct, a fundamental problem with the SR model is that statistical relevance information is insufficient to fully capture causal information in the sense that different causal structures can be consistent with the same information about statistical relevance relationships. Similarly, the CM model faces the difficulty that information about causal processes and interactions is also insufficient to fully capture causal relevance relations and that there is a range of cases in which causal relationships hold between C and E (and hence in which C figures in an explanation of E) although there is no connecting causal process between C and E. Finally, a fundamental problem with unificationist models is that the content of our causal judgments does not seem to fall out of our efforts at unification, at least when unification is understood along the lines advocated by Kitcher. For example, as discussed above, considerations having to do with unification do not by themselves explain why it is appropriate to explain effects in terms of their causes rather than vice-versa.