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
L'inférence de la causalité est une problématique récurrente pour un large éventail de domaines où l...
This paper articulates an account of causation as a collection of information-theoretic relationship...
I examine the adequacy of the causal graph-structural equations approach to causation for modeling b...
AbstractI study the interplay between stochastic dependence and causal relations within the setting ...
We use a notion of causal independence based on intervention, which is a fundamental concept of the ...
The need to measure causal influences between random variables or processes in complex networks aris...
We propose different approaches to infer causal influences between agents in a network using only ob...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
This dissertation studies the definition, identification, and estimation of causal effects within th...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
Explanations in Bayesian networks are usually probabilistic measures of how well a hypothesis is sup...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
This article proposes an extension of the well-known concept of Granger causality, called GB-Granger...
The broad abundance of time series data, which is in sharp contrast to limited knowledge of the unde...
This work examines an information theoretic quantity known as directed information, which measures ...
L'inférence de la causalité est une problématique récurrente pour un large éventail de domaines où l...
This paper articulates an account of causation as a collection of information-theoretic relationship...
I examine the adequacy of the causal graph-structural equations approach to causation for modeling b...
AbstractI study the interplay between stochastic dependence and causal relations within the setting ...
We use a notion of causal independence based on intervention, which is a fundamental concept of the ...
The need to measure causal influences between random variables or processes in complex networks aris...
We propose different approaches to infer causal influences between agents in a network using only ob...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
This dissertation studies the definition, identification, and estimation of causal effects within th...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
Explanations in Bayesian networks are usually probabilistic measures of how well a hypothesis is sup...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
This article proposes an extension of the well-known concept of Granger causality, called GB-Granger...
The broad abundance of time series data, which is in sharp contrast to limited knowledge of the unde...
This work examines an information theoretic quantity known as directed information, which measures ...
L'inférence de la causalité est une problématique récurrente pour un large éventail de domaines où l...
This paper articulates an account of causation as a collection of information-theoretic relationship...
I examine the adequacy of the causal graph-structural equations approach to causation for modeling b...
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