It is argued that in deterministic contexts evidence for causal relations states whether a boundary condition makes a difference or not to a phenomenon. In order to substantiate the analysis, I show that this difference/indifference making is the basic type of evidence required for eliminative induction in the tradition of Francis Bacon and John Stuart Mill. To this purpose, an account of eliminative induction is proposed with two distinguishing features: it includes a method to establish the causal irrelevance of boundary (...) conditions by means of indifference making, which is called strict method of agreement, and it introduces the notion of a background against which causal statements are evaluated. Causal statements thus become three-place-relations postulating the relevance or irrelevance of a circumstance C to the examined phenomenon P with respect to a background B of further conditions. To underline the importance of evidence in terms of difference/indifference making, I sketch two areas, in which eliminative induction is extensively used in natural and engineering sciences. One concerns exploratory experiments, the other engineering design methods. Given that a method is discussed that has been used for centuries, I make no claims to novelty in this paper, but hope that the combined discussion of several topics that are still somewhat underrepresented in the philosophy of science literature is of some merit. (shrink)

In this paper we try to bring some clarification in the recent debate on causal pluralism. Our first aim is to clarify what it means to have a pluralistic theory of causation and to articulate the criteria by means of which a certain theory of causation can or cannot qualify as a pluralistic theory of causation. We also show that there is currently no theory on the\nmarket which meets these criteria, and therefore no full-blown pluralist theory of causation exists. Because (...) of this, we offer a general strategy by means of which pluralist theories of causation can be developed. (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)

We present a logic, \, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of deterministic causal regularities is, as (...) Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting \, we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare \ with the CNA algorithm that was recently proposed by Michael Baumgartner. (shrink)

All univocal analyses of causation face counterexamples. An attractive response to this situation is to become a pluralist about causal relationships. "Causal pluralism" is itself, however, a pluralistic notion. In this article, I argue in favor of pluralism about concepts of cause in the social sciences. The article will show that evidence for, inference from, and the purpose of causal claims are very closely linked. Key Words: causation • pluralism • evidence • methodology.

Diogenes, Ahead of Print. This analytic autoethnographic and autobiographical essay addresses several interrelated questions regarding the use of ethnographic and otherwise ‘qualitative’ research methods in the study of contemporary urban society. The testy relationship between qualitative and quantitative research has historical as well as logico-deductive roots that continue to haunt the social sciences. As to hermeneutics, the debate parallels my academic career journey from Indiana University to Brooklyn College by way of New York University during which I learned that the (...) normative practices of the social and not so social sciences come in a myriad of different competing, and occasionally conflicting, pre- and proscriptions. My intention in this essay is not to construct a fine, or even a crude, philosophical discourse but to argue for more attention to be paid to what social scientists do best as opposed to the labels they apply to each other and their trades. As the positivist founders of sociology would agree, social science is not exempt from the laws of social science. At the conclusion, an example will be given of the kind of knowledge accessible only through direct observation and best conveyed by thick description. (shrink)

This analytic autoethnographic and autobiographical essay addresses several interrelated questions regarding the use of ethnographic and otherwise ‘qualitative’ research methods in the study of contemporary urban society. The testy relationship between qualitative and quantitative research has historical as well as logico-deductive roots that continue to haunt the social sciences. As to hermeneutics, the debate parallels my academic career journey from Indiana University to Brooklyn College by way of New York University during which I learned that the normative practices of the (...) social and not so social sciences come in a myriad of different competing, and occasionally conflicting, pre- and proscriptions. My intention in this essay is not to construct a fine, or even a crude, philosophical discourse but to argue for more attention to be paid to what social scientists do best as opposed to the labels they apply to each other and their trades. As the positivist founders of sociology would agree, social science is not exempt from the laws of social science. At the conclusion, an example will be given of the kind of knowledge accessible only through direct observation and best conveyed by thick description. (shrink)

The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the fact (...) that causes be defined in terms of ‘difference-making’, this cannot fixes the causal metaphysics. Causality in epidemiology ought to be interpreted according to the epistemic theory. In this approach relations are deemed causal depending on the evidence and on the available methods. Indeed, evidence to establish causal claims requires difference-making considerations; furthermore, those definitions of cause reflect the ‘variational’ epistemology and methodology of epidemiology. (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)