 By contrast, Pearl (2000) and Woodward (2007) point to an aspect of structural causal models that may make the framework fit less well with at least some aspects of theorizing in physics. Structural causal models make perspicuous the tight connections between the notions of cause and intervention or manipulation. A structural causal model provides us with information on how the values of variables change under external interventions into a system, while causal discovery algorithms allow us to construct causal models from information about probability distributions over the values of variables characterizing the system and information about the effects of interventions. On Woodward’s account the notion of causation is spelled out in terms of so-called “hard” or “arrow-breaking” interventions. Arrow-breaking interventions allow us to investigate how changes to a variable V percolate through a system, when we place V under the full control of an intervention variable and break all other causal arrows into V. Yet arrow-breaking interventions may not be possible within the context of certain physical theories. Newtonian gravitational forces, for example, cannot be turned off.