
Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency
Recursive linear structural equation models are widely used to postulate...
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On the SemiMarkov Equivalence of Causal Models
The variability of structure in a finite Markov equivalence class of cau...
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Causal Identification under Markov Equivalence
Assessing the magnitude of causeandeffect relations is one of the cent...
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Identification of Strong Edges in AMP Chain Graphs
The essential graph is a distinguished member of a Markov equivalence cl...
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Size of Interventional Markov Equivalence Classes in Random DAG Models
Directed acyclic graph (DAG) models are popular for capturing causal rel...
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Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Graphical models are popular statistical tools which are used to represe...
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PermutationBased Causal Structure Learning with Unknown Intervention Targets
We consider the problem of estimating causal DAG models from a mix of ob...
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Minimal enumeration of all possible total effects in a Markov equivalence class
In observational studies, when a total causal effect of interest is not identified, the set of all possible effects can be reported instead. This typically occurs when the underlying causal DAG is only known up to a Markov equivalence class, or a refinement thereof due to background knowledge. As such, the class of possible causal DAGs is represented by a maximally oriented partially directed acyclic graph (MPDAG), which contains both directed and undirected edges. We characterize the minimal additional edge orientations required to identify a given total effect. A recursive algorithm is then developed to enumerate subclasses of DAGs, such that the total effect in each subclass is identified as a distinct functional of the observed distribution. This resolves an issue with existing methods, which often report possible total effects with duplicates, namely those that are numerically distinct due to sampling variability but are in fact causally identical.
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