 Conditional probability analyses do not proceed by assigning truth-conditions to (all) counterfactuals. They instead associate them with certain conditional probabilities.[41] This makes it difficult to integrate the theory into a comprehensive compositional semantics and logic for a natural language. Kaufmann (2005 2008) makes important advances here, but it remains an open issue for conditional probability analyses. Leitgeb (2012a,b) thoroughly develops a new conditional probability analysis which regards \(\mathsf{\phi&gt;\psi}\) as true when the relevant conditional probability is sufficiently high.[42] But conditional probability analyses have other limitations. Without further development, these analyses are limited in their ability to explain how humans judge particular counterfactuals to be true. There is a large literature in psychology, beginning with Kahneman, Slovic, and Tversky 1982, showing that human reasoning diverges in predictable way from precise probabilistic reasoning. Even if these performance differences didn’t turn up in counterfactuals and conditional probabilities, there is an implementation issue. As discussed in §1.2.3, directly implementing probabilistic knowledge makes unreasonable demands on memory. Bayesian Networks are one proposed solution to this implementation issue. They are also used in the analysis of causal dependence (§1.3), which conditional probability analyses must appeal to anyway. Since Bayesian Networks can also be used to directly formulate a semantics of counterfactuals, they provide an worthwhile alternative to conditional probability analyses despite proceeding from similar assumptions.