 These authors generalize the notion of intervention in two ways. First, they consider interventions that do not deterministically fix the value of variable(s) intervened on but rather merely impose a probability distribution on those variables. Second, they explore the use of what have come to be called “soft” interventions. These are interventions that unlike the fully surgical (“hard”) interventions considered above (both Pearl’s setting interventions and the notion associated with  M1–M4),  do not completely break the previously existing relationships between the variable \(X\) intervened on and its causes \(C\) but rather supply an exogenous source \(I\) of variation to \(X\) that leaves its relations to \(C\) intact but where \(I\) is uncorrelated with \(C\). Certain experiments are naturally modeled in this way. For example, in an experiment in which subjects are randomly given various amounts of additional income (besides whatever income they have from other sources) this additional income functions as a soft, rather than a hard intervention. Soft interventions may be possible in practice or in principle in certain situations in which hard interventions are not. Eberhardt 2007 and Eberhardt and Scheines 2007 explore what can be learned from various combinations of soft and hard, indeterministic and deterministic interventions together with non-experimental data in various contexts. Unsurprisingly each kind of intervention and associated data have both advantages and limitations from the point of view of inference.