Concrete Causation

Roland Poellinger (MCMP/LMU) gives a talk at the workshop on "Mechanisms in Medicine" (3-5 July, 2017) titled "Probabilistic Causal Inference from Heterogeneous Evidence" (based on joint work with Barbara Osimani and Jürgen Landes). Abstract: Current methods for the purpose of causal inference aim to deliver a categorical assessment about the presence of a causal relationship between events or variables. This is at odds with the great amount of epistemic and ethical uncertainty surrounding most applied sciences. In particular, for the sake of the precautionary principle, this uncertainty should not be dismissed but rather explicitly accounted for in detecting, preventing and managing, e.g., environmental or health hazards (Kreibel et al. 2001, Raffensperger, and Tickner 1999). The rationale for the attenuation of the requirement of scientific proof and certainty about the causal link is ultimately one of minimizing expected loss by anticipating risk detection and prevention (Osimani & Russo 2016; Osimani, Russo, Williamson, 2011). We here present a framework for causal assessment which allows the incorporation of heterogeneous pieces of evidence via a probabilistic judgement about the causal link between candidate causes and effects (Landes, Osimani, Poellinger, 2017). The framework comes in the form of a Bayesian network whose nodes represent epistemic variables related to causal associations. In particular, our system i) identifies possible indicators of causality on the basis of the methodological and philosophical literature on causality, evidence, and causal inference; ii) embeds them in a topological framework of probabilistic dependencies and independencies grounded in assumptions regarding their reciprocal epistemic interconnections; iii) weakly orders some of these probabilistic dependencies as a function of their inferential strength with respect to the confirmation of causal hypotheses. This system has been developed for the purpose of drug safety assessment, but it can be easily applied to other domains with relatively few adjustments. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance (external validity), and reliability (see for instance, Howick 2011, Clarke et al. 2014, Cartwright 2011, Teira 2011). In this talk we will discuss how mechanistic knowledge together with statistical properties, information about difference-making, and evidence of the temporal structure can jointly support a causal hypothesis in extrapolating from study to target.

Michael Strevens (NYU) meets Roland Poellinger (MCMP/LMU) in a joint session on "Unifying Causal and Non-Causal Knowledge" at the MCMP workshop "Bridges 2014" (2 and 3 Sept, 2014, German House, New York City). The 2-day trans-continental meeting in mathematical philosophy focused on inter-theoretical relations thereby connecting form and content of this philosophical exchange. Idea and motivation: We use theories to explain, to predict and to instruct, to talk about our world and order the objects therein. Different theories deliberately emphasize different aspects of an object, purposefully utilize different formal methods, and necessarily confine their attention to a distinct field of interest. The desire to enlarge knowledge by combining two theories presents a research community with the task of building bridges between the structures and theoretical entities on both sides. Especially if no background theory is available as yet, this becomes a question of principle and of philosophical groundwork: If there are any – what are the inter-theoretical relations to look like? Will a unified theory possibly adjudicate between monist and dualist positions? Under what circumstances will partial translations suffice? Can the ontological status of inter-theoretical relations inform us about inter-object relations in the world? Find more about the meeting at www.lmu.de/bridges2014.

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.

As part of the MCMP group presentation at the DGPhil XXII Workshop on Mathematical Philosophy Roland Poellinger (Munich Center for Mathematical Philosophy/LMU Munich) gives a mini presentation titled "Disentangling Nets for Causal Inference", in which he motivates an extension of standard Bayes net causal models to also allow for the embedding of non-causal knowledge. A longer introduction to the framework of Causal Knowledge Patterns (CKPs) can be found in the recording of the talk "Computing Non-Causal Knowledge for Causal Reasoning". The video "The Mind-Brain Entanglement" contains an application of CKPs in the philosophy of mind.

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".