REVIEW 2 cited by
The causal manipulation of chain event graphs
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Discrete Bayesian Networks have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that generalises the class of discrete BN models. It provides a flexible and expressive framework for representing and analysing the implications of causal hypotheses, expressed in terms of the effects of a manipulation of the generating underlying system. We prove that, as for a BN, identifiability analyses of causal effects can be performed through examining the topology of the CEG graph, leading to theorems analogous to the back-door theorem for the BN.
Forward citations
Cited by 2 Pith papers
-
Partially Observed Structural Causal Models
POSCMs extend structural causal models to latent contexts that co-determine both graph structure and mechanisms, supported by an identifiability theory and validation in a retina simulator.
-
Partially Observed Structural Causal Models
POSCMs extend SCMs to settings where the causal graph itself is generated by latent context and can be intervened on, with conditional kernel-identifiability theorems and illustrative retina simulations.
Discussion (0). Sign in to comment.