Add to ORKGThe 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
Discovering causal relations among latent variables in directed acyclic graphical model
The possibility question concerns the status of possibilities: do they form an irreducible category ...
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...
The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores...
Abstract. Bayes nets are seeing increasing use in expert systems [2, 6], and structural equations mo...
We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
Recent accounts of actual causation are stated in terms of extended causal models. These extended ca...
Attempts to characterize people's causal knowledge of a domain in terms of causal network struc...
Abstract: "The problem of inferring causal relations from statistical data in the absence of experim...
Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized he...
In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments ...
According to the transitive dynamics model, people can construct causal structures by linking togeth...
This dissertation studies how the mechanism-based view of causality can assist in construction and u...
Recent research in cognitive and developmental psy-chology on acquiring and using causal knowledge u...
Discovering causal relations among latent variables in directed acyclic graphical model
The possibility question concerns the status of possibilities: do they form an irreducible category ...
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...
The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores...
Abstract. Bayes nets are seeing increasing use in expert systems [2, 6], and structural equations mo...
We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
Recent accounts of actual causation are stated in terms of extended causal models. These extended ca...
Attempts to characterize people's causal knowledge of a domain in terms of causal network struc...
Abstract: "The problem of inferring causal relations from statistical data in the absence of experim...
Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized he...
In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments ...
According to the transitive dynamics model, people can construct causal structures by linking togeth...
This dissertation studies how the mechanism-based view of causality can assist in construction and u...
Recent research in cognitive and developmental psy-chology on acquiring and using causal knowledge u...
Discovering causal relations among latent variables in directed acyclic graphical model
The possibility question concerns the status of possibilities: do they form an irreducible category ...
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...