This paper investigates the use of Boolean techniques in a systematic study of cause-effect relationships. The model uses partially defined Boolean functions. Procedures are provided to extrapolate from limited observations, concise and meaningful theories to explain the effect under study, and to prevent (or provoke) its occurrenc
We describe a method that infers whether statistical dependences between two observed variables X an...
International audienceThis book presents ground-breaking advances in the domain of causal structure ...
Causal structure discovery is a much-studied topic and a fundamental problem in Machine Learning. Ca...
peer reviewedThis paper investigates the use of Boolean techniques in a systematic study of cause-ef...
A mathematical model of the sufficient-component cause framework is considered based on the theories...
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...
Four theories proposing determinate relations of actual causation for Boolean networks are described...
The sufficient cause framework describes how sets of sufficient causes are responsible for causing s...
The discovery of causal relationships from empirical data is an important problem in machine learnin...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
International audienceThis chapter addresses the problem of benchmarking causal models or validating...
The causal relationships determining the behaviour of a system under study are inherently directiona...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
We describe a method that infers whether statistical dependences between two observed variables X an...
International audienceThis book presents ground-breaking advances in the domain of causal structure ...
Causal structure discovery is a much-studied topic and a fundamental problem in Machine Learning. Ca...
peer reviewedThis paper investigates the use of Boolean techniques in a systematic study of cause-ef...
A mathematical model of the sufficient-component cause framework is considered based on the theories...
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...
Four theories proposing determinate relations of actual causation for Boolean networks are described...
The sufficient cause framework describes how sets of sufficient causes are responsible for causing s...
The discovery of causal relationships from empirical data is an important problem in machine learnin...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
International audienceThis chapter addresses the problem of benchmarking causal models or validating...
The causal relationships determining the behaviour of a system under study are inherently directiona...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
We describe a method that infers whether statistical dependences between two observed variables X an...
International audienceThis book presents ground-breaking advances in the domain of causal structure ...
Causal structure discovery is a much-studied topic and a fundamental problem in Machine Learning. Ca...