Add to ORKGThis paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between the two models with respect to model expansions. This asymmetry might constitute a basis on which to disambiguate corresponding causal inferences on non-empirical ground
As the Chain Event Graph (CEG) has a topology which represents sets of conditional independence stat...
We consider two variables that are related to each other by an invertible function. While it has pre...
Over the past twenty-five years, a large number of algorithms have been developed to learn the struc...
While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are c...
While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are c...
This thesis aims to show that explicit understanding of possible causal structures often aids in inf...
This paper seeks to cast light on some of the more puzzling aspects of causation. My initial aim is ...
The first part of this paper reveals a conflict between the core principles of deterministic causati...
Previous work suggests that humans find it difficult to learn the structure of causal systems given ...
AbstractThe Markov condition describes the conditional independence relations present in a causal mo...
Causal theories of action, perception and knowledge are each beset by problems of so-called ‘deviant...
Linear structural causal models (SCMs) -- in which each observed variable is generated by a subset o...
Learning causal structure from observational data often assumes that we observe independent and iden...
The paper looks at the conditional independence search approach to causal discovery, proposed by Spi...
When performing causal discovery, assumptions have to be made on how the true causal mechanism corre...
As the Chain Event Graph (CEG) has a topology which represents sets of conditional independence stat...
We consider two variables that are related to each other by an invertible function. While it has pre...
Over the past twenty-five years, a large number of algorithms have been developed to learn the struc...
While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are c...
While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are c...
This thesis aims to show that explicit understanding of possible causal structures often aids in inf...
This paper seeks to cast light on some of the more puzzling aspects of causation. My initial aim is ...
The first part of this paper reveals a conflict between the core principles of deterministic causati...
Previous work suggests that humans find it difficult to learn the structure of causal systems given ...
AbstractThe Markov condition describes the conditional independence relations present in a causal mo...
Causal theories of action, perception and knowledge are each beset by problems of so-called ‘deviant...
Linear structural causal models (SCMs) -- in which each observed variable is generated by a subset o...
Learning causal structure from observational data often assumes that we observe independent and iden...
The paper looks at the conditional independence search approach to causal discovery, proposed by Spi...
When performing causal discovery, assumptions have to be made on how the true causal mechanism corre...
As the Chain Event Graph (CEG) has a topology which represents sets of conditional independence stat...
We consider two variables that are related to each other by an invertible function. While it has pre...
Over the past twenty-five years, a large number of algorithms have been developed to learn the struc...