Causal representations are distinguished from non-causal ones by their ability to predict the results of interventions. This widely-accepted view suggests the following adequacy condition for causal models: a causal model is adequate only if it does not contain variables regarding which it makes systematically false predictions about the results of interventions. Here I argue that this condition should be rejected. For a class of equilibrium systems, there will be two incompatible causal models depending on whether one intervenes upon a certain (...) variable to fix its value, or ‘lets go’ of the variable and allows it to vary. The latter model will fail to predict the result of interventions on the let-go-of variable. I argue that there is no basis for preferring one of these models to the other, and thus that models failing to predict interventions on particular variables can be just as adequate as those making no such false predictions. This undermines a key argument against relying upon causal models inferred from equilibrium data. (shrink)

Causal representations are distinguished from non-causal ones by their ability to predict the results of interventions. This widely-accepted view suggests the following adequacy condition for causal models: a causal model is adequate only if it does not contain variables regarding which it makes systematically false predictions about the results of interventions. Here I argue that this condition should be rejected. For a class of equilibrium systems, there will be two incompatible causal models depending on whether one intervenes upon a certain (...) variable to fix its value, or ‘lets go’ of the variable and allows it to vary. The latter model will fail to predict the result of interventions on the let-go-of variable. I argue that there is no basis for preferring one of these models to the other, and thus that models failing to predict interventions on particular variables can be just as adequate as those making no such false predictions. This undermines a key argument (Dash in Caveats for causal reasoning with equilibrium models. University of Pittsburgh. PhD thesis, 2003) against relying upon causal models inferred from equilibrium data. (shrink)

The recent literature on causality has seen the introduction of several distinctions within causality, which are thought to be important for understanding the widespread scientific practice of focusing causal explanations on a subset of the factors that are causally relevant for a phenomenon. Concepts used to draw such distinctions include, among others, stability, specificity, proportionality, or actual-difference making. In this contribution, I propose a new distinction that picks out an explanatorily salient class of causes in biological systems. Some select causes (...) in complex biological systems, I argue, have the property of enabling coherent causal control of these systems. Examples of such control variables include hormones and other signaling molecules, e.g., TOR (target of rapamycin), morphogens or the products of homeotic selector genes in embryonic pattern formation. I propose an analysis of this notion based on concepts borrowed from causal graph theory. (shrink)