 A great deal of recent work on causation has used systems of equations and directed graphs to represent causal relationships. Judea Pearl (e.g., Pearl 2009) is an influential example of this approach. His work provides a striking illustration of the heuristic usefulness of a manipulationist framework in specifying what it is to give such systems a causal  interpretation.[1]  Pearl characterizes the notion of an intervention by reference to a primitive notion of a causal mechanism. A functional causal model is a system of equations \(X_i = F(Pa_i, U_i)\) where \(Pa_i\) represents the parents or direct causes of \(X_i\) that are explicitly included in the model and \(U_i\) represents an error variable that summarizes the impact of all excluded variables. Each equation represents a distinct causal mechanism which is understood to be “autonomous” in the sense in which that notion is used in econometrics; this means roughly that it is possible to interfere with or disrupt each mechanism (and the corresponding equation) without disrupting any of the others. The simplest sort of intervention in which some variable \(X_i\) is set to some particular value \(x_i\) amounts, in Pearl’s words, to 