Organisation: Michał Sikorski, Alexander Gebharter, and Barbara Osimani
Time: 15:30 – 19:00, September 12, 2024
The workshop brings together experts on causation in order to combine discussions of causation from a metaphysics but also from a more methodological perspective. The workshop is part of the Salzburg Conference for Young Analytic Philosophy (SOPhiA 2024). For further information see the conference website: https://sophia-conference.org/
Speakers:
Lorenzo Casini (University of Bologna)
Alexander Gebharter (CPSP, Marche Polytechnic University)
Vera Hoffmann-Kolss (University of Bern)
Barbara Osimani (CPSP, Marche Polytechnic University)
Michał Sikorski (CPSP, Marche Polytechnic University)
Naftali Weinberger (MCMP, Ludwig Maximilian University Munich)
Program:
| Time | Room SR 1.004 |
| 15:30 | Downward causation and the epiphenomenalist revenge, Lorenzo Casini |
| 16:10 | Causal Models and the Proportionality Constraint, Vera Hoffmann-Kolss |
| 16:50 | Break |
| 17:00 | Thinking Outside the Box: Causation and Variable-Relativity, Alexander Gebharter & Barbara Osimani |
| 17:40 | Redefining Representativeness in Causal Terms, Michał Sikorski, Alexander Gebharter & Barbara Osimani |
| 18:20 | Homeostasis and the faithless foundations of causal inference, Naftali Weinberger |
Titles & Abstracts:
Lorenzo Casini & Alexander Gebharter (University of Bologna & CPSP, Marche Polytechnic University): Downward causation and the epiphenomenalist revenge
Woodward (2003) has offered a well-known analysis of causation in terms of the notion of an ideal intervention. His (2003) definition of an ideal intervention was later modified (2015) in an attempt to justify mental causation. The move was criticized as it gives rise to “inherent underdetermination” (Baumgartner 2017). To counter the objection, Woodward (2020, 2022) has recently proposed the addition of a condition of “realization independence” (RI) to the theory. We argue that this move is problematic. In possible contexts where there is no downward causation, in fact, RI can be used to derive contradictions either with the principle that motivated Woodward’s (2015) revised definition of an ideal intervention or with the core intuition of interventionism that there is causation if, and only if, there is a difference-making relation under a possible intervention. This dilemma renders Woodward’s position vulnerable to what we call the “epiphenomenalist revenge”: the best explanation for why it is so difficult to justify mental causation without running into contradictions is that mental causation does not exist.
Alexander Gebharter & Barbara Osimani (CPSP, Marche Polytechnic University): Thinking outside the box: Causation and variable-relativity
More methodological accounts of causation usually operate on a selection of variables manageable for a human being or computer. We argue that in laying out the conditions under which they correctly represent causal structure, such accounts require reference to a notion of causation that is not relativized to a specific set of variables. We develop such a non-relativized account of causation and show how it can provide a unifying conceptual basis for more methodological accounts. We explicitly discuss causally interpreted Bayesian networks, structural equation models, and Woodward’s (2003) interventionist account of causation.
Vera Hoffmann-Kolss (University of Bern): Causal models and the proportionality constraint
A crucial question in the contemporary debate about causation is at what level causal relations occur, that is, whether causation occurs only at the physical level or also at higher levels, such as the biological or psychological levels. A prominent anti-reductionist approach is based on proportionality considerations: causes should be proportional to their effects, that is, have the same level of grain as their effects. In this paper, I examine this proportionality constraint from the perspective of a causal modeling approach. I discuss several challenges that arise for proportionality requirements in the context of multi-level causal models, and explore how causal models can still be used to describe causal relations that occur at different levels.
Michał Sikorski, Alexander Gebharter & Barbara Osimani (CPSP, Marche Polytechnic University): Redefining representativeness of a sample in causal terms
In a typical medical experiment, it is not feasible to test all members of the target population. Because of this, scientists need to rely on a sample. This approach can be successful only if the sample, at least to some degree, represents the target population—in other words, if it is representative. Surprisingly, despite its crucial role, representativeness remains a controversial topic in the methodology of medical science. It is not only unclear how to best define representativeness, but also whether it is worth aiming at representativeness in the first place. In this paper, we intend to present a new definition of representativeness, formulated in terms of causally interpreted Bayesian networks. We will argue that this new definition is not only more precise but also provides more guidance than existing alternatives.
Naftali Weinberger (MCMP, Ludwig Maximilian University Munich): Homeostasis and the faithless foundations of causal inference
The causal faithfulness condition, which licenses inferences from probabilistic to causal independence, is known to be violated in dynamical systems exhibiting homeostasis. Using the example of the Watt governor, I here present a precise causal characterization of such violations, which differ from cases involving cancelling paths. Traditional defenses of faithfulness do not carry over to cases of homeostasis and one should expect such cases to be widespread. Epistemically, this does not pose a problem for causal inference, since such failures of faithfulness do not undermine the ability of models at different time scales to properly represent the causal and probabilistic independence relations effectively obtaining at the relevant time scale. These failures are nevertheless significant, since they provide the key to understanding how causal attributions can apply within complex systems exhibiting high degrees of mutual dependence among their parts.
Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1 — Call for tender No. 1409/2022 of 14/09/2022 of the Italian Ministry of University and Research funded by the European Union – NextGenerationEU. Award Number: P2022RT4AT_001, Concession Decree No. 1371 of 01/09/2023 adopted by the Italian Ministry of University and Research, CUP I53D23006890001, Controlling and Utilizing Uncertainty in the Health Sciences.