 Hitchcock (2001, 2007) and Woodward (2003: Ch.5) develop counterfactual analyses of causation using the tools of Bayesian networks (or “causal models”) and structural equations described back in  §1.2.3.  The rough idea of the analysis is as follows. Given a graph like the one in  Figure 2,  X can be said to be a cause of Y just in case there is a path from X to Y and changing just the value of X changes the value of Y. According to Hitchcock (2001) and Woodward (2002, 2003), this analysis of causation counts as a counterfactual analysis because the basic structural equations, e.g., \(C\dequal A\land B\), are best understood as primitive counterfactual claims, e.g., if A and B had been true, C would have been true. While not all theories of causation that employ structural equations are counterfactual theories, structural equations are central to many of the contemporary counterfactual theories of  causation.[13]  See  counterfactual theories of causation  for further developments and critical reactions to this account of causation.