We investigate the algebra and geometry of general interventions in discrete DAG models. To this end, we develop the formalism to study these models as subvarieties of multiprojective space and introduce a theory for modeling soft interventions in the more general family of staged tree models. We then consider the problem of finding their defining equations, and we derive a combinatorial criterion for identifying interventional staged tree models for which the defining ideal is toric. This criterion, when combined with a new characterization of decomposable DAG models in terms of their associated staged trees, specializes to a graphical criterion in the case of discrete interventional DAG models.Comment: Comments welcom
We establish conditions under which latent causal graphs are nonparametrically identifiable and can ...
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approx...
Probabilistic inference in graphical models is the task of computing marginal and conditional densit...
© 2019 by the author(s). Directed acyclic graph (DAG) models are popular for capturing causal relati...
A staged tree model is a discrete statistical model encoding relationships between events. These mod...
Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of d...
Directed Acyclic Graphs (DAGs) are a powerful tool to model the network of dependencies among variab...
The main feature of the paper is to show that Algebraic Statistics is a natural framework to addres...
A well-studied challenge that arises in the structure learning problem of causal directed acyclic gr...
This dissertation develops the mathematical formalism to analyse now established staged tree models ...
© 2018 Elsevier B.V. DAG models are statistical models satisfying a collection of conditional indepe...
Characteristic imsets are 0-1 vectors which correspond to Markov equivalence classes of directed acy...
Algebraic geometry is used to study properties of a class of discrete distributions defined on trees...
We conjecture that the worst case number of experiments necessary and sufficient to discover a causa...
We consider the problem of estimating causal DAG models from a mix of observational and intervention...
We establish conditions under which latent causal graphs are nonparametrically identifiable and can ...
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approx...
Probabilistic inference in graphical models is the task of computing marginal and conditional densit...
© 2019 by the author(s). Directed acyclic graph (DAG) models are popular for capturing causal relati...
A staged tree model is a discrete statistical model encoding relationships between events. These mod...
Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of d...
Directed Acyclic Graphs (DAGs) are a powerful tool to model the network of dependencies among variab...
The main feature of the paper is to show that Algebraic Statistics is a natural framework to addres...
A well-studied challenge that arises in the structure learning problem of causal directed acyclic gr...
This dissertation develops the mathematical formalism to analyse now established staged tree models ...
© 2018 Elsevier B.V. DAG models are statistical models satisfying a collection of conditional indepe...
Characteristic imsets are 0-1 vectors which correspond to Markov equivalence classes of directed acy...
Algebraic geometry is used to study properties of a class of discrete distributions defined on trees...
We conjecture that the worst case number of experiments necessary and sufficient to discover a causa...
We consider the problem of estimating causal DAG models from a mix of observational and intervention...
We establish conditions under which latent causal graphs are nonparametrically identifiable and can ...
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approx...
Probabilistic inference in graphical models is the task of computing marginal and conditional densit...