A mathematical model of the sufficient-component cause framework is considered based on the theories of Boolean algebra. The model consists of the space of states of a binary experiment and a set of symmetries of the experiment. The space of states is a Boolean algebra of n Boolean variables where n is the number of the binary causes in the experiment. The set of symmetries of the experiment is a subgroup of the group of all automorphisms of Boolean algebra of the states of experiment. This subgroup is generated by transformations preserving a type of interaction. An experimenter should deduce these transformations from the peculiar properties of the experiment. Examples of such transformations are provided. Classification of interactions i...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
The Hilbert space effect algebra is a fundamental mathematical structure which is used to describe u...
While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are c...
Sufficient-component causes are discussed within the potential outcome framework so as to formalize ...
Definitions are given for weak and strong sufficient cause interactions in settings in which the out...
peer reviewedThis paper investigates the use of Boolean techniques in a systematic study of cause-ef...
A sufficient cause interaction between two exposures signals the presence of individuals for whom th...
Abstract Background Sufficient-cause interaction is a type of interaction that has received much att...
The sufficient cause framework describes how sets of sufficient causes are responsible for causing s...
The aim of this thesis is to develop and explore models in, and related to, the sufficient cause fra...
Sufficient cause interactions concern cases in which there is a particular causal mechanism for some...
For decades, the sufficient cause model and the counterfactual model have shaped our understanding o...
We provide a scheme for inferring causal relations from uncontrolled statistical data based on tools...
A causal algebra and its application to high energy physics is proposed. Firstly on the basis of qua...
Four theories proposing determinate relations of actual causation for Boolean networks are described...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
The Hilbert space effect algebra is a fundamental mathematical structure which is used to describe u...
While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are c...
Sufficient-component causes are discussed within the potential outcome framework so as to formalize ...
Definitions are given for weak and strong sufficient cause interactions in settings in which the out...
peer reviewedThis paper investigates the use of Boolean techniques in a systematic study of cause-ef...
A sufficient cause interaction between two exposures signals the presence of individuals for whom th...
Abstract Background Sufficient-cause interaction is a type of interaction that has received much att...
The sufficient cause framework describes how sets of sufficient causes are responsible for causing s...
The aim of this thesis is to develop and explore models in, and related to, the sufficient cause fra...
Sufficient cause interactions concern cases in which there is a particular causal mechanism for some...
For decades, the sufficient cause model and the counterfactual model have shaped our understanding o...
We provide a scheme for inferring causal relations from uncontrolled statistical data based on tools...
A causal algebra and its application to high energy physics is proposed. Firstly on the basis of qua...
Four theories proposing determinate relations of actual causation for Boolean networks are described...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
The Hilbert space effect algebra is a fundamental mathematical structure which is used to describe u...
While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are c...