Published online by Cambridge University Press: 01 January 2022
Whether y obtains under the counterfactual supposition that x is thought to depend on whether y obtains in the most similar world(s) in which x obtains. Graphical causal models have proved useful in developing a principled notion of similarity between worlds, but this notion is limited insofar as it does not apply to counterfactual suppositions about causal structure. Here, we explore the possibility of filling this lacuna by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which graphs can be similar, we introduce multiple similarity metrics and multiple ways to prioritize these metrics.
To contact the authors, please write to: Benjamin Eva, University of Konstanz, 78464 Konstanz, Germany; e-mail: email@example.com. Reuben Stern, Kansas State University, 1116 Mid Campus Dr. North, 201 Dickens Hall, Manhattan, KS 66506; e-mail: firstname.lastname@example.org. Stephan Hartmann, Munich Center for Mathematical Philosophy, Ludwig Maximilian University of Munich, 80539 Munich, Germany; e-mail: email@example.com.