 Similarity and strict analyses nowhere refer to facts, or propositions, depending on each other. Indeed, 1979 was primarily concerned with explaining which true counterfactuals, given a similarity analysis, manifest a relation of counterfactual dependence. Other analyses have instead started with the idea that facts depend on each other, and then explain how these relations of dependence make counterfactuals true. As will become clear, none of these analyses endorse the naive idea that \(\mathsf{A &gt; B}\) is true only when B counterfactually depends on A. The dependence can be more complex, indirect, or B could just be true and independent of A. Theories in this family differ crucially in how they model counterfactual dependence. In premise semantics (§3.1) dependence is modeled in terms of how facts, which are modeled as parts of worlds, are distributed across a space of worlds that has been constrained by laws, or law-like generalizations. In probabilistic semantics (§3.2), this dependence is modeled as some form of conditional probability. In Bayesian networks, structural equations, and causal models (§3.3), it is modeled in terms of the Bayesian networks discussed at the beginning of §1.2.3. Because theories of these three kinds are very much still in development and often involve even more sophisticated formal models than those covered in §2, this section will have to be more cursory than §2 to ensure breadth and accessibility.