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The Similarity of Causal Structure

Eva, Benjamin and Stern, Reuben and Hartmann, Stephan (2018) The Similarity of Causal Structure. [Preprint]

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Does y obtain under the counterfactual supposition that x? The answer to this question is famously thought to depend on whether y obtains in the most similar world(s) in which x obtains. What this notion of ‘similarity’ consists in is controversial, but in recent years, graphical causal models have proved incredibly useful in getting a handle on considerations of similarity between worlds. One limitation of the resulting conception of similarity is that it says nothing about what would obtain were the causal structure to be different from what it actually is, or from what we believe it to be. In this paper, we explore the possibility of using graphical causal models to resolve counterfactual queries about causal structure by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which a graph G* can be more similar to a graph G than a graph G**, we introduce multiple similarity metrics, as well as multiple ways to prioritize the various metrics when settling counterfactual queries about causal structure.

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Item Type: Preprint
Hartmann, Stephans.hartmann@lmu.de0000-0001-8676-2177
Keywords: Causation, Counterfactuals, Belief Revision, Causal Bayesian Networks
Subjects: General Issues > Causation
Specific Sciences > Probability/Statistics
Depositing User: Dr Benjamin Eva
Date Deposited: 19 Jul 2018 10:41
Last Modified: 12 Jul 2024 17:23
Item ID: 14884
Subjects: General Issues > Causation
Specific Sciences > Probability/Statistics
Date: 19 July 2018

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