According to manipulationist accounts of causal explanation, to explain an event is to show how it could be changed by intervening on its cause. The relevant change must be a ‘serious possibility’ claims Woodward 2003, distinct from mere logical or physical possibility—approximating something I call ‘scientific possibility’. This idea creates significant difficulties: background knowledge is necessary for judgments of possibility. Yet the primary vehicles of explanation in manipulationism are ‘invariant’ generalisations, and these are not well adapted to encoding such (...) knowledge, especially in the social sciences, as some of it is non-causal. Ceteris paribus (CP) laws or generalisations labour under no such difficulty. A survey of research methods such as case and comparative studies, randomised control trials, ethnography, and structural equation modeling, suggests that it would be more difficult and in some instances impossible to try to represent the output of each method in invariant generalisations; and that this is because in each method causal and non-causal background knowledge mesh in a way that cannot easily be accounted for in manipulationist terms. Ceteris paribus-generalisations being superior in this regard, a theory of explanation based on the latter is a better fit for social science. (shrink)

A new way of avoiding the causal exclusion argument in the context of manipulationism is proposed. In manipulationism, causal explanations are defined by counterfactual information accessed through manipulations. It is argued that the property of manipulability can be an emergent property of aggregate systems. Therefore, some causal explanations are non-reducible and causal exclusion is avoided. This emergentist notion of causal explanation addresses the question of how the special sciences can be based upon causal reasoning, even if fundamental physics (...) is absent of causal relations. (shrink)

Many researchers consider cancer to have molecular causes, namely mutated genes that result in abnormal cell proliferation (e.g. Weinberg 1998); yet for others, the causes of cancer are to be found not at the molecular level but at the tissue level and carcinogenesis would consist in a disrupted tissue organization with downward causation effects on cells and cellular components (e.g. Sonnenschein & Soto 2008). In this contribution, I ponder how to make sense of such downward causation claims. Adopting a manipulationist (...) account of causation (Woodward 2003), I propose a formal definition of downward causation, and discuss further requirements (in light of Baumgartner 2009). I then show that such an account cannot be mobilized in support of non-reductive physicalism (contrary to Raatikainen 2010). However, I also argue that such downward causation claims might point at particularly interesting dynamic properties of causal relationships that might prove salient in characterizing causal relationships (following Woodward 2010). (shrink)

Many researchers consider cancer to have molecular causes, namely mutated genes that result in abnormal cell proliferation (e.g. Weinberg 1998). For others, the causes of cancer are to be found not at the molecular level but at the tissue level where carcinogenesis consists of disrupted tissue organization with downward causation effects on cells and cellular components (e.g. Sonnenschein and Soto 2008). In this contribution, I ponder how to make sense of such downward causation claims. Adopting a manipulationist account of causation (...) (Woodward 2003), I propose a formal definition of downward causation and discuss further requirements (in light of Baumgartner 2009). I then show that such an account cannot be mobilized in support of non-reductive physicalism (contrary to Raatikainen 2010). However, I also argue that such downward causation claims might point at particularly interesting dynamic properties of causal relationships that might prove salient in characterizing causal relationships (following Woodward 2010). (shrink)

Manipulationism holds that information about the results of interventions is of utmost importance for scientific practices such as causal assessment or explanation. Specifically, manipulation provides information about the stability, or invariance, of the relationship between X and Y: were we to wiggle the cause X, the effect Y would accordingly wiggle and, additionally, the relation between the two will not be disrupted. This sort of relationship between variables are called 'invariant empirical generalisations'. The paper focuses on questions about causal (...) assessment and analyses the status of manipulation. It is argued that manipulationism is trapped in a dilemma. If manipulationism is read as providing a conceptual analysis of causation, then it fails to provide a story about the methods for causal assessment. If, instead, manipulationism is read as providing a method for causal assessment, then it is at an impasse concerning causal assessment in areas where manipulations are not performed. Empirical generalisations are then reassessed, in such a way that manipulation is not taken as methodologically fundamental. The paper concludes that manipulation is the appropriate tool for some scientific contexts, but not for all. (shrink)

The literature on the nature of understanding can be divided into two broad camps. Explanationists believe that it is knowledge of explanations that is key to understanding. In contrast, their manipulationist rivals maintain that understanding essentially involves an ability to manipulate certain representations. The aim of this paper is to provide a novel knowledge based account of understanding. More specifically, it proposes an account of maximal understanding of a given phenomenon in terms of fully comprehensive and maximally well-connected knowledge of (...) it and of degrees of understanding in terms of approximations to such knowledge. It is completed by a contextualist semantics for outright attributions of understanding according to which an attribution of understanding is true of one just in case one knows enough about it to perform some contextually determined task. It is argued that this account has an edge over both its explanationist and manipulationist competitors. (shrink)

The interventionist account of causal explanation, in the version presented by Jim Woodward, has been recently claimed capable of buttressing the widely felt—though poorly understood—hunch that high-level, relatively abstract explanations, of the sort provided by sciences like biology, psychology and economics, are in some cases explanatorily optimal. It is the aim of this paper to show that this is mistaken. Due to a lack of effective constraints on the causal variables at the heart of the interventionist causal-explanatory scheme, as presently (...) formulated it is either unable to prefer high-level explanations to low, or systematically overshoots, recommending explanations at so high of a level as to be virtually vacuous. (shrink)

Causation has always been a philosophically controversial subject matter. While David Hume’s empiricist account of causation has been the dominant influence in analytic philosophy and science during modern times, a minority view has instead connected causation essentially to agency and manipulation. A related approach has for the first time gained widespread popularity in recent years, due to new powerful theories of causal inference in science that are based in a technical notion of intervention, and James Woodward’s closely connected interventionist theory (...) of causation in philosophy. This monograph assesses five manipulationist or interventionist theories of causation, viewed as theories that purport to tell us what causation is by providing us with the meaning of causal claims. It is shown that they cannot do this, as the conditions on causation that they impose are too weak, mainly due to ineliminable circularities in their definitions of causal terms. It is then argued that a subset of Woodward’s theory can nevertheless contribute crucially to an explanation of the unique role that manipulation has in our acquisition of causal knowledge. This explanation differs from the common regularist explanation of the epistemic utility of manipulation and experiment, and it is taken to confirm several important manipulationist intuitions. However, the success of the explanation depends on (this subset of) interventionism not itself being understood as a theory of causation, but as a theory of intervention. (shrink)

A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This paper, narrowing down the scope to quantitative causal analysis in social science, reformulates the problem in terms of the validity of statistical models. Two strategies to make sense of correlational data are presented: first, a 'structural strategy', the goal of which (...) is to model and test causal structures that explain correlational data; second, a 'manipulationist or interventionist strategy', that hinges upon the notion of invariance under intervention. It is argued that while the former can offer a solution the latter cannot. (shrink)

Price’s :157–176, 1991; 44:187–203, 1993 ; 2007, 2017) agency theory of causation has takes itself to provide a use-theory of our causal discourse. The theory’s aim is to describe the rules implicit to our linguistic behaviour when we describe things in causal terms. According to this theory, the rules governing our use of the concept of causation are based on our perspective as agents and our associated experiences of manipulating events. I argue that the observed relation between agency and our (...) concept of causation cannot exhaustively describe the conditions under which we enter into causal discourse. In particular, I demonstrate that the agency theory faces familiar problems with accounting for causal ascriptions to token cases. (shrink)

Asymptotic models in which singular limits are taken are very common in physics. They are often used to investigate the general behaviour of systems undergoing rapid, discontinuous, changes. The singularities in the mathematics of these systems have no physical counterparts; these models operate by containing non-physically interpretable fictional elements. As such there is an intuition that states that asymptotics only offer descriptions of systems not explanations of them. By contrast, in different areas of science other models containing fictional elements which (...) are physically interpretable are claimed to be explanatory. I argue that both types of fictional model possess modal content, and therefore it is unclear why models that contain unphysical fictions are merely descriptions, but models that contain physical fictions are explanations. One must either reject both as unexplanatory because they use false ontology to explain, or one should accept both types as explanatory. (shrink)