 The distinctively causal or “structural” content of a SEM derives from the way in which interventions are represented. To intervene on a variable is to set the value of that variable by a process that overrides the usual causal structure, without interfering with the causal processes governing the other variables. More precisely, an intervention on a variable X overrides the normal equation for X, while leaving the other equations unchanged. For example, to intervene on the variable Flame in our example would be to set the flame to a specified level regardless of whether the igniter is pressed or what the gas level is. (Perhaps, for example, one could pour kerosene into the grill and light it with a match.) Woodward (2003) proposes that we think of an intervention as a causal process that operates independently of the other variables in the model. Randomized controlled trials aim to intervene in this sense. For example, a randomized controlled trial to test the efficacy of a drug for hypertension aims to determine whether each subject takes the drug (rather than a placebo) by a random process such as a coin flip. Factors such as education and health insurance that normally influence whether someone takes the drug no longer play this role for subjects in the trial population. Alternately, we could follow the approach of Lewis (1979) and think of an intervention setting the value of a variable by a minor “miracle”.