 In the previous section, we discussed how to use knowledge (or assumptions) about the structure of a causal graph \(\bG\) to make inferences about the results of interventions. In this section, we explore the converse problem. If we can intervene on variables and observe the post-intervention probability distribution, what can we infer about the underlying causal structure? This topic has been explored extensively in the work of Eberhardt and his collaborators. (See, for example, Eberhardt &amp; Scheines 2007 and Hyttinen et al. 2013a.) Unsurprisingly, we can learn more about causal structure if we can perform interventions than if we can only make passive observations. However, just how much we can infer depends upon what kinds of interventions we can perform, and on what background assumptions we make.