In his study of causation J. L. Mackie once referred back to David Hume, who listed causation among one of the principles that are TO US THE CEMENT OF THE UNIVERSE and thus OF VAST CONSEQUENCE IN THE SCIENCE OF HUMAN NATURE (David Hume, AN ABSTRACT OF A “TREATISE OF HUMAN NATURE”). Yet for example the early endeavours of the developers of the Structural Equation Modelling (SEM) framework, which aimed at embedding causal meaning into the formal treatment, seem to be neglected, and David Lewis' counterfactual analysis of causation based on his possible worlds semantics does not come very handy for application. As Judea Pearl summarises: WE ARE WITNESSING ONE OF THE MOST BIZARRE CIRCLES IN THE HISTORY OF SCIENCE: CAUSALITY IN SEARCH OF A LANGUAGE AND, SIMULTANEOUSLY, THE LANGUAGE OF CAUSALITY IN SEARCH OF ITS MEANING (Judea Pearl, CAUSALITY, 2000). Borrowing mathematical rigour from statistics, one of the most prominent areas of causal modelling today sounds out the interaction of probabilistic and deterministic approaches and is centred around Bayesian Networks, through which causal notions can be identified concretely and utilised for various disciplines eventually.
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In his study of causation J. L. Mackie once referred back to David Hume, who listed causation among one of the principles that are TO US THE CEMENT OF THE UNIVERSE and thus OF VAST CONSEQUENCE IN THE SCIENCE OF HUMAN NATURE (David Hume, AN ABSTRACT OF A “TREATISE OF HUMAN NATURE”). Yet for example the early endeavours of the developers of the Structural Equation Modelling (SEM) framework, which aimed at embedding causal meaning into the formal treatment, seem to be neglected, and David Lewis' counterfactual analysis of causation based on his possible worlds semantics does not come very handy for application. As Judea Pearl summarises: WE ARE WITNESSING ONE OF THE MOST BIZARRE CIRCLES IN THE HISTORY OF SCIENCE: CAUSALITY IN SEARCH OF A LANGUAGE AND, SIMULTANEOUSLY, THE LANGUAGE OF CAUSALITY IN SEARCH OF ITS MEANING (Judea Pearl, CAUSALITY, 2000). Borrowing mathematical rigour from statistics, one of the most prominent areas of causal modelling today sounds out the interaction of probabilistic and deterministic approaches and is centred around Bayesian Networks, through which causal notions can be identified concretely and utilised for various disciplines eventually.
Roland Poellinger (MCMP/LMU) gives a talk at the MCMP Colloquium (14 May, 2014) titled "The Mind-Brain Entanglement". Abstract: Listing "The Nonreductivist’s Troubles with Mental Causation" (1993) Jaegwon Kim suggested that the only remaining alternatives are the eliminativist’s standpoint or plain denial of the mind’s causal powers if we want to uphold the closure of the physical and reject causal overdetermination at the same time. Nevertheless, explaining stock market trends by referring to investors’ fear of loss is a very familiar example of attributing reality to both domains and acknowledging the mind’s interaction with the world: "if you pick a physical event and trace its causal ancestry or posterity, you may run into mental events" (Kim 1993). In this talk I will use the formal framework of Bayes net causal models in an interventionist understanding (as devised, e.g., by Judea Pearl in "Causality", 2000) to make the concept of causal influence precise. Investigating structurally similar cases of conflicting causal intuitions will motivate a natural extension of the interventionist Bayes net framework, Causal Knowledge Patterns, in which our intuition that the mind makes a difference finds an expression.
Concrete Causation
In his study of causation J. L. Mackie once referred back to David Hume, who listed causation among one of the principles that are TO US THE CEMENT OF THE UNIVERSE and thus OF VAST CONSEQUENCE IN THE SCIENCE OF HUMAN NATURE (David Hume, AN ABSTRACT OF A “TREATISE OF HUMAN NATURE”). Yet for example the early endeavours of the developers of the Structural Equation Modelling (SEM) framework, which aimed at embedding causal meaning into the formal treatment, seem to be neglected, and David Lewis' counterfactual analysis of causation based on his possible worlds semantics does not come very handy for application. As Judea Pearl summarises: WE ARE WITNESSING ONE OF THE MOST BIZARRE CIRCLES IN THE HISTORY OF SCIENCE: CAUSALITY IN SEARCH OF A LANGUAGE AND, SIMULTANEOUSLY, THE LANGUAGE OF CAUSALITY IN SEARCH OF ITS MEANING (Judea Pearl, CAUSALITY, 2000). Borrowing mathematical rigour from statistics, one of the most prominent areas of causal modelling today sounds out the interaction of probabilistic and deterministic approaches and is centred around Bayesian Networks, through which causal notions can be identified concretely and utilised for various disciplines eventually.
