Intervention, determinism, and the causal minimality condition

Synthese 182 (3):335-347 (2011)
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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



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Author Profiles

Peter Spirtes
Carnegie Mellon University
Jiji Zhang
Chinese University of Hong Kong

Citations of this work

Causal Exclusion and Causal Bayes Nets.Alexander Gebharter - 2017 - Philosophy and Phenomenological Research 95 (2):353-375.
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References found in this work

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.

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