This talk will present a simple approach to unifying these two approaches via a new graph, termed the Single-World Intervention Graph (SWIG). The SWIG encodes the counterfactual independences associated with a specific hypothetical intervention on a set of treatment variables. The nodes on the SWIG are the corresponding counterfactual random variables. The SWIG is derived from a causal DAG via a simple node-splitting transformation. The theory will be illustrated with a number of examples. Finally, it will be shown that SWIGs avoid a number of pitfalls that are present in an alternative approach to unification, based on “twin networks,” that has been advocated by Pearl (2000).