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(2016) Synthese 193 (4).
The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores the set of causal variables that function as relata in these axioms. Spirtes (2007) showed how a causal system can be equivalently described by two different sets of variables that stand in a non-trivial translation-relation to each other, suggesting that there is no “correct” set of causal variables. I extend Spirtes’ result to the general framework of linear structural equation models and then explore to what extent the possibility to intervene or a preference for simpler causal systems may help in selecting among sets of causal variables.
Publication details
DOI: 10.1007/s11229-015-0832-z
Full citation:
Eberhardt, F. (2016). Green and grue causal variables. Synthese 193 (4), pp. 1029-1046.
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