Intervention, determinism, and the causal minimality condition
Document Type
Journal article
Source Publication
Synthese
Publication Date
10-1-2011
Volume
182
Issue
3
First Page
335
Last Page
347
Publisher
Springer Netherlands
Keywords
Causation, Causal Bayesian network, Determinism, Markov condition, Intervention, Probability
Abstract
We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather than an add-on methodological assumption of simplicity, necessarily follows from the substantive interventionist theses, provided that the actual probability distribution is strictly positive. Second, we demonstrate that the causal minimality condition can fail when the actual probability distribution is not positive, as is the case in the presence of deterministic relationships. But we argue that the interventionist account still entails a pragmatic justification of the causal minimality condition. Our argument in the second part exemplifies a general perspective that we think commendable: when evaluating methods for inferring causal structures and their underlying assumptions, it is relevant to consider how the inferred causal structure will be subsequently used for counterfactual reasoning.
DOI
10.1007/s11229-010-9751-1
Print ISSN
00397857
E-ISSN
15730964
Publisher Statement
Copyright © 2010 Springer Science+Business Media B.V.
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Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Zhang, J., & Spirtes, P. (2011). Intervention, determinism, and the causal minimality condition. Synthese, 182(3), 335-347. doi: 10.1007/s11229-010-9751-1