Add to ORKGAbstract—Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty. Its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject. This essay examines the notion of subjectivity by drawing parallels between Lacanian theory and Bayesian probability theory, and concludes that the latter must be enriched with causal interventions to model agency. The central contribution of this work is an abstract model of the subject that accommodates causal interventions in a measure-theoretic formalisation that is more general than causal graphs. This formalisation is obtained through a game-theoretic Ansatz base...
Correctly assessing the consequences of events is essential for a successful interaction with the wo...
Humans possess considerable causal knowledge about the world. For example, one might have beliefs ab...
Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disci...
Discovering causal relationships is a hard task, often hindered by the need for intervention, and of...
A fundamental issue for theories of human induction is to specify constraints on potential inference...
Discovering causal relationships is a hard task, often hindered by the need for intervention, and of...
Although no universally accepted definition of causality exists, in practice one is often faced with...
Discovering causal relationships is a hard task, often hindered by the need for intervention, and of...
AbstractIn this article, we demonstrate the usefulness of causal Bayesian networks as probabilistic ...
I present a formalism that combines two methodologies: *objective Bayesianism* and *Bayesian nets*. ...
An intervention is a tool that enables us to distinguish between causality and simple correlation. T...
The rationality of human causal judgments has been the focus of a great deal of recent research. We ...
International audienceAn intervention is a tool that enables us to distinguish between causality and...
International audienceAn intervention is a tool that enables us to distinguish between causality and...
People often struggle when making Bayesian probabilistic estimates on the basis of competing sources...
Correctly assessing the consequences of events is essential for a successful interaction with the wo...
Humans possess considerable causal knowledge about the world. For example, one might have beliefs ab...
Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disci...
Discovering causal relationships is a hard task, often hindered by the need for intervention, and of...
A fundamental issue for theories of human induction is to specify constraints on potential inference...
Discovering causal relationships is a hard task, often hindered by the need for intervention, and of...
Although no universally accepted definition of causality exists, in practice one is often faced with...
Discovering causal relationships is a hard task, often hindered by the need for intervention, and of...
AbstractIn this article, we demonstrate the usefulness of causal Bayesian networks as probabilistic ...
I present a formalism that combines two methodologies: *objective Bayesianism* and *Bayesian nets*. ...
An intervention is a tool that enables us to distinguish between causality and simple correlation. T...
The rationality of human causal judgments has been the focus of a great deal of recent research. We ...
International audienceAn intervention is a tool that enables us to distinguish between causality and...
International audienceAn intervention is a tool that enables us to distinguish between causality and...
People often struggle when making Bayesian probabilistic estimates on the basis of competing sources...
Correctly assessing the consequences of events is essential for a successful interaction with the wo...
Humans possess considerable causal knowledge about the world. For example, one might have beliefs ab...
Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disci...