Graphs as Models of Interventions

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.

Roland Poellinger (Munich Center for Mathematical Philosophy/LMU Munich) gives a talk at the MCMP Workshop on Computational Metaphysics titled "Computing Non-Causal Knowledge for Causal Reasoning". Abstract: We use logical and mathematical knowledge to generate causal claims. Inter-definitions or semantic overlap cannot be consistently embedded in standard Bayes net causal models since in many cases the Markov requirement will be violated. These considerations motivate an extension of Bayes net causal models to also allow for the embedding of Epistemic Contours (ECs). Such non-causal functions are consistently computable in Causal Knowledge Patterns (CKPs). An application of the framework can be found, e.g., in the recording of the talk "The Mind-Brain Entanglement".

In this talk at the LMU workshop "Concrete Causation" (9 July, 2010) Jan-Willem Romeijn (Groningen) discusses probabilistic models of experimental intervention, and shows that such models elucidate the intuition that observations following intervention are more informative than observations per se (due to technical problems about one minute of the recording is skipped)

In this talk Roland Poellinger (Munich) gives an outline of Judea Pearl's deterministic approach towards causation (workshop "Concrete Causation", 9 July, 2010). The title of the talk is taken from the programmatic section 2.2 of Pearl's paper "Causal Diagrams for Empirical Research" (Biometrika, Vol. 82, No. 4, 669-709, 1995) which is briefly sketched and commented on as an introduction to Pearl's interventionist account of causal analysis. Further topics: The problems of simple causal networks, interventions as variables, Humphreys' paradox, and causal decision making.

In this talk Mathias Frisch (University of Maryland and Humboldt scholar at LMU Munich) critically examines a range of general arguments for the view that causal notions have an important place in the special sciences and discusses a case of causal modeling in physics - linear response theory (workshop "Concrete Causation", 9 July, 2010).

The poster of the workshop "Concrete Causation" (9 July, 2010) with all speakers, times, chairs, and breaks