Alexander Gebharter

Constitutive Relevance, Mutual Manipulability, and Fat-Handedness

The first part of this article argues that if Craver’s ([2007a], [2007b]) popular mutual manipulability account (MM) of mechanistic constitution is embedded within Woodward’s ([2003]) interventionist theory of causation—for which it is explicitly designed—it either undermines the mechanistic research paradigm by entailing that there do not exist relationships of constitutive relevance, or it gives rise to the unwanted consequence that constitution is a form of causation. The second part shows how Woodward’s theory can be adapted in such a way that MM neither undermines the mechanistic paradigm nor reduces constitution to causation. However, it turns out that this modified theoretical embedding of MM makes it impossible to produce empirical evidence for constitutive relations. The article ends by suggesting an additional criterion—the fat-handedness criterion—which, when combined with MM generates indirect empirical evidence for constitutive relevance. 1 Introduction 2 Mechanisms and Constitutive Relevance 3 Mutual Manipulability and Interventionism 4 Modifying Interventionism 5 Fat-Handedness 6 Conclusion 1 Introduction 2 Mechanisms and Constitutive Relevance 3 Mutual Manipulability and Interventionism 4 Modifying Interventionism 5 Fat-Handedness 6 Conclusion

Causality as a theoretical concept: explanatory warrant and empirical content of the theory of causal nets

We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be understood as a theoretical concept that is not explicated by a single definition, but by the axioms of a theory. Such an understanding of causality implicitly underlies the well-known theory of causal (Bayes) nets (TCN) and has been explicitly promoted by Glymour (Br J Philos Sci 55:779–790, 2004). In this paper we investigate the explanatory warrant and empirical content of TCN. We sketch how the assumption of directed cause–effect relations can be philosophically justified by an inference to the best explanation. We then ask whether the explanations provided by TCN are merely post-facto or have independently testable empirical content. To answer this question we develop a fine-grained axiomatization of TCN, including a distinction of different kinds of faithfulness. A number of theorems show that although the core axioms of TCN are empirically empty, extended versions of TCN have successively increasing empirical content.

A Formal Framework for Representing Mechanisms

In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the phenomena the mechanism brings about. Finally, I sketch an alternative approach taking (i) and (ii) into account.

Causal Graphs and Biological Mechanisms

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice shows the need for quantitative, probabilistic models of mechanisms, too. In this chapter, we argue that the formal framework of causal graph theory is well suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the basis of an example from contemporary biological practice, namely, feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multilevel character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes while being insufficient for others.

A modeling approach for mechanisms featuring causal cycles

Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal representation of mechanisms. Gebharter’s “A Formal Framework for Representing Mechanisms?” suggests to represent mechanisms by means of arrows in an acyclic causal net. In this article we show how this approach can be extended in such a way that it can also be fruitfully applied to mechanisms featuring causal feedback.

Causality as a Theoretical Concept

In the first part of this chapter I finish the axiomatization of the causal nets framework started in Chap. 3 I also argue that the causal Markov axiom provides the best explanation for two statistical phenomena. In the second part I present several results about the empirical content of different versions (i.e., combination of axioms) of the theory of causal nets. Both parts together show that causation satisfies the same modern standards as theoretical concepts of good empirical theories do. This can be seen as new empirical support for the theory of causal nets, but also as an answer to Hume’s skeptical challenge: Actually, it seems that we have good reasons to believe in causation as something ontologically real out there in the world.

A causal Bayesian network model of disease progression mechanisms in chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a cancer of the hematopoietic system initiated by a single genetic mutation which results in the oncogenic fusion protein Bcr-Abl. Untreated, patients pass through different phases of the disease beginning with the rather asymptomatic chronic phase and ultimately culminating into blast crisis, an acute leukemia resembling phase with a very high mortality. Although many processes underlying the chronic phase are well understood, the exact mechanisms of disease progression to blast crisis are not yet revealed. In this paper we develop a mathematical model of CML based on causal Bayesian networks in order to study possible disease progression mechanisms. Our results indicate that an increase of Bcr-Abl levels alone is not sufficient to explain the phenotype of blast crisis and that secondary changes such as additional mutations are necessary to explain disease progression and the poor therapy response of patients in blast crisis.

Causal Nets and Mechanisms

In this chapter I enter the new mechanist debate within the philosophy of science. I discuss a proposal how to model mechanisms made by Casini, Illari, Russo, and Williamson and present three problems with their approach. I then present my alternative approach of how to represent mechanisms, compare it with Casini et al.’s approach, and discuss Casini’s recent objections to my approach. I also make a suggestion how constitutive relevance relations could be represented within my approach. In the rest of this chapter I extend the approach in such a way that it can also account for the diachronic character of mechanisms and the fact that many mechanisms feature causal cycles.

Causal Nets and Woodwardian Interventionism

In this chapter I develop a novel reconstruction of Woodward’s interventionist theory of causation within the theory of causal nets. This endeavor allows one to see in which respects the two theories agree and in which respects they diverge from each other. It also allows for uncovering several weak points of Woodward’s theory which may have been overlooked otherwise. I highlight some of these weak points of Woodward’s interventionist theory of causation and suggest several modifications of the theory to avoid them. This results in two alternative versions of an interventionist theory. The basic causal notions of these alternative versions turn out to coincide with the corresponding causal notions within the theory of causal nets under suitable conditions.

Introduction to the special issue “Causation, probability, and truth—the philosophy of Clark Glymour”

This special issue of Synthese is in honor of Clark Glymour, who is a key figure in philosophy of science since decades. Clark’s early work focused on more traditional issues in the philosophy of science such as confirmation, scientific theories, and general relativity. Later he also worked on historical topics in psychiatry and physics. Together with his student Kevin Kelly, he investigated formal learning theory as a tool for learning about formal theories. But the contribution to the philosophy of science probably most philosophers immediately associate with Clark Glymour is the causal interpretation of Bayes nets, which he developed together with his students Peter Spirtes and Richard Scheines at around 1990. The theory of causal Bayes nets carefully connects causal structure to empirical data via several axioms. This, for the first time in the history of philosophy, allowed researchers to get a grasp on causation from an empirical point of view. Awhole new and revolutionary research programwas born: The theory provided a framework for developing feasible algorithms for causal search under various conditions, but also for developing methods for computing the effects of interventions even if only non-experimental data is available. This special issue partially documents the results of a two-day symposium with the title “The philosophy of Clark Glymour”, which was organized at the University of Düsseldorf by Matthias Unterhuber and the guest editors of this special issue. The symposium was the peak of Clark’s fellowship at the Düsseldorf Center for Logic and Philosophy of Science (DCLPS) in June 2013. It was a wonderful time for which we are very grateful. Clark is an impressive person with a formidable character. It is hard to describe the “big old guy with the hat” in a few words. Hence, we try our best in describing this special issue instead, which might be a much easier endeavor. Some of