In their rich and intricate paper ‘Independence, Invariance, and the Causal Markov Condition’, Daniel Hausman and James Woodward ([1999]) put forward two independent theses, which they label ‘level invariance’ and ‘manipulability’, and they claim that, given a specific set of assumptions, manipulability implies the causal Markov condition. These claims are interesting and important, and this paper is devoted to commenting on them. With respect to level invariance, I argue that Hausman and Woodward's discussion is confusing because, as I point out, (...) they use different senses of ‘intervention’ and ‘invariance’ without saying so. I shall remark on these various uses and point out that the thesis is true in at least two versions. The second thesis, however, is not true. I argue that in their formulation, the manipulability thesis is patently false and that a modified version does not fare better. Furthermore, I think their proof that manipulability implies the causal Markov condition is not conclusive. In the deterministic case it is valid but vacuous, whereas it is invalid in the probabilistic case. 1 Introduction 2 Intervention, invariance and modularity 3 The causal Markov condition: CM1 and CM2 4 From MOD to the causal Markov condition and back 5 A second argument for CM2 6 The proof of the causal Markov condition for probabilistic causes 7 ‘Cartwright's objection’ defended 8 Metaphysical defenses of the causal Markov condition 9 Conclusion. (shrink)

We currently have on offer a variety of different theories of causation. Many are strikingly good, providing detailed and plausible treatments of exemplary cases; and all suffer from clear counterexamples. I argue that, contra Hume and Kant, this is because causation is not a single, monolithic concept. There are different kinds of causal relations imbedded in different kinds of systems, readily described using thick causal concepts. Our causal theories pick out important and useful structures that fit some familiar cases—cases we (...) discover and ones we devise to fit. (shrink)

Glennan appeals to interventions to solve the ontological and explanatory regresses that threaten his mechanistic account of causality . I argue that Glennan’s manoeuvre fails. The appeal to interventions is not able to address the ontological regress, and it blocks the explanatory regress only at the cost of making the account inapplicable to non-modular mechanisms. I offer a solution to the explanatory regress that makes use of dynamic Bayesian networks. My argument is illustrated by a case study from systems biology, (...) namely, the mechanism for the irreversibility of apoptosis. I conclude by pointing out the implications of my argument for Glennan’s mechanistic account of causality and, more generally, for accounts of mechanistic explanation based on interventions. 1 Introduction2 Glennan’s Account of Causality3 Objections to Glennan’s Account4 Glennan’s Replies5 Ontological Symmetry?6 Explanatory Symmetry?7 A Solution to the Explanatory Regress8 The Prospects of Glennan’s Account. (shrink)

Recently, a number of philosophers of science have claimed that much explanation in the sciences, especially in the biomedical and social sciences, is mechanistic explanation. I argue the account of mechanistic explanation provided in this tradition has not been entirely satisfactory, as it has neglected to describe in complete detail the crucial causal structure of mechanistic explanation. I show how the interventionist approach to causation, especially within a structural equations framework, provides a simple and elegant account of the causal structure (...) of mechanisms. This account explains the many useful insights of traditional accounts of mechanism, such as Carl Craver's account in his book Explaining the Brain , but also helps to correct the omissions of such accounts. One of these omissions is the failure to provide an explicit formulation of a modularity constraint that plays a significant role in mechanistic explanation. One virtue of the interventionist/structural equations framework is that it allows for a simple formulation of a modularity constraint on mechanistic explanation. I illustrate the role of this constraint in the last section of the paper, which describes the form that mechanistic explanation takes in the computational, information-processing paradigm of cognitive psychology. (shrink)

Nancy Cartwright's recent criticisms of efforts and methods to obtain causal information from sample data using automated search are considered. In addition to reviewing that effort, I argue that almost all of her criticisms are false and rest on misreading, overgeneralization, or neglect of the relevant literature.

Mechanisms are often characterized as causal structures and the interventionist account of causation is then used to characterize what it is to be a causal structure. The associated modularity constraint on causal structures has evoked criticism against using the theory as an account of mechanisms, since many mechanisms seem to violate modularity. This paper answers to this criticism by making a distinction between a causal system and a causal structure. It makes sense to ask what the modularity properties of a (...) given causal structure are, but not whether a causal system is modular tout court. The counter-examples to the interventionist account are systems in which a particular structure is modular in variables, but not in parameters. A failure of parameter-modularity does not by itself threaten the interventionist interpretation of the structure and the possibility of causally explaining with that structure, but it does mean that knowledge of the structure is not sufficient to constitutively explain system-level properties of the embedding system. (shrink)

In this thesis I investigate causal inquiry in the social sciences, drawing on examples from various disciplines and in particular from conflict studies. In a backlash against the pervasiveness of statistical methods, in the last decade certain social scientists have focused on finding the causal mechanisms behind observed correlations. To provide evidence for such mechanisms, researchers increasingly rely on ‘process tracing’, a method which attempts to give evidence for causal relations by specifying the chain of events connecting a putative cause (...) and effect of interest. I will ask whether the causal claims process tracers make are defensible, and where they are not defensible I will ask how we can improve the method. Throughout these investigations, I show that the conclusions of process tracing are constrained both by the causal structure ofthe social world and by social scientists’ aims and values. My central argument is this: all instances of social phenomena have causally relevant differences, which implies that any research design that requires some comparison between cases is limited by how we systematize these phenomena. Moreover, such research cannot rely on stable regularities. Nevertheless, to forego causal conclusions altogether is not the right response to these limitations; by carefully outlining our epistemic assumptions we can make progress in causal inquiry. While I use philosophical theories of causation to comment on the feasibility of a social scientific method, I also do the reverse: by investigating a popular contemporary method in the social sciences, I show to what extent our philosophical theories of causation are workable in practice. Thus, this thesis is both a methodological and a philosophical work. Every chapter discusses both a fundamental philosophical position on the social sciences and a relevant case study from the social sciences. (shrink)