AbstractI study the interplay between stochastic dependence and causal relations within the setting of Bayesian networks and in terms of information theory. The application of a recently defined causal information flow measure provides a quantitative refinement of Reichenbach’s common cause principle
Humans possess considerable causal knowledge about the world. For example, one might have beliefs ab...
Synchronization, a basic nonlinear phenomenon, is widely observed in diverse complex systems studied...
Although no universally accepted definition of causality exists, in practice one is often faced with...
AbstractI study the interplay between stochastic dependence and causal relations within the setting ...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
We use a notion of causal independence based on intervention, which is a fundamental concept of the ...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
The common cause principle says that every correlation is either due to a direct causal effect linki...
The need to measure causal influences between random variables or processes in complex networks aris...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
This dissertation studies the definition, identification, and estimation of causal effects within th...
Accurately determining dependency structure is critical to discovering a system's causal or...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
Modeling a causal association as arising from a communication process between cause and effect, simp...
submitted, minor changes, different presentation of simulation resultsThis paper deals with the stud...
Humans possess considerable causal knowledge about the world. For example, one might have beliefs ab...
Synchronization, a basic nonlinear phenomenon, is widely observed in diverse complex systems studied...
Although no universally accepted definition of causality exists, in practice one is often faced with...
AbstractI study the interplay between stochastic dependence and causal relations within the setting ...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
We use a notion of causal independence based on intervention, which is a fundamental concept of the ...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
The common cause principle says that every correlation is either due to a direct causal effect linki...
The need to measure causal influences between random variables or processes in complex networks aris...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
This dissertation studies the definition, identification, and estimation of causal effects within th...
Accurately determining dependency structure is critical to discovering a system's causal or...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
Modeling a causal association as arising from a communication process between cause and effect, simp...
submitted, minor changes, different presentation of simulation resultsThis paper deals with the stud...
Humans possess considerable causal knowledge about the world. For example, one might have beliefs ab...
Synchronization, a basic nonlinear phenomenon, is widely observed in diverse complex systems studied...
Although no universally accepted definition of causality exists, in practice one is often faced with...
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