Cet article défend l’idée que l’on peut distinguer quatre niveaux d’analyse au sein des sciences sociales (y compris l’économie) – à savoir, a) un niveau descriptif statistique, b) un niveau explicatif causal, c) un niveau explicatif téléologique, et d) un niveau téléologique prescriptif. Généralement, les sciences sociales ne considèrent que les niveaux a) et b). L’exclusion du niveau c) peut conduire à considérer comme des « anomalies » certains comportements qui ne sont pas compatibles avec des théories comme celle du (...) choix rationnel ou celle de l’utilité espérée – à savoir, des théories qui adoptent une attitude dite de « rationalité instrumentale ». Inclure le niveau c) dans la réflexion implique de considérer la « rationalité pratique » et cela rend ces anomalies « raisonnables ». L’article prend pour cadre théorique la notion de causalité et la téléologie d’Aristote. La première section introduit ces notions, tandis que la deuxième section explore les points de vue contemporains sur la causalité et la téléologie. La troisième section introduit des arguments pour établir la légitimité de l’usage des valeurs en sciences sociales. Cette étape est nécessaire, car les causes finales des explications téléologiques dans le domaine des choses humaines sont bien, en définitive, des valeurs. Ces discussions ouvrent la voie à l’introduction, dans la quatrième et dernière section, des quatre niveaux analytiques susmentionnés. Plus précisément, dans le cas de l’économie, cette discussion se fonde sur la classification des disciplines économiques chez Carl Menger. (shrink)

Die Einleitung unseres Kapitels bietet eine grundsäzliche Charakterisierung der Soziologie und zeichnet einige wichtige historische Entwicklungslinien der Philosophie der Soziologie (PdS) nach. Im Hauptteil werden zentrale ontologische sowie ausgewählte explanatorische Themen der PdS vorgestellt. Im Schlussteil sollen einige aktuelle Diskussionen umrissen werden.

Researchers misunderstand their role in creating ethical problems when they allow dogmas to purportedly divorce scientists and scientific practices from the values that they embody. Cortina, Edwards, and Powell help us clarify and further develop our position by responding to our critique of, and alternatives to, this misleading separation. In this rebuttal, we explore how the desire to achieve the separation of facts and values is unscientific on the very terms endorsed by its advocates—this separation is refuted by empirical observation. (...) We show that positivists like Cortina and Edwards offer no rigorous theoretical or empirical justifications to substantiate their claims, let alone critique ours. Following Powell, we point to how classical pragmatism understands ‘purpose’ in scientific pursuits while also providing an alternative to the dogmas of positivism and related philosophical positions. In place of dogmatic, unscientific cries about an abstract and therefore always-unobservable ‘reality,’ we invite all organizational scholars to join us in shifting the discussion about quantitative research towards empirically grounded scientific inquiry. This makes the ethics of actual people and their practices central to quantitative research, including the thoughts, discourses, and behaviors of researchers who are always in particular places doing particular things. We propose that quantitative researchers can thus start to think about their research practices as a kind of work, rather than having the status of a kind of dogma. We conclude with some implications that this has for future research and education, including the relevance of research and research methods. (shrink)

Quantitative researchers often discuss research ethics as if specific ethical problems can be reduced to abstract normative logics (e.g., virtue ethics, utilitarianism, deontology). Such approaches overlook how values are embedded in every aspect of quantitative methods, including ‘observations,’ ‘facts,’ and notions of ‘objectivity.’ We describe how quantitative research practices, concepts, discourses, and their objects/subjects of study have always been value-laden, from the invention of statistics and probability in the 1600s to their subsequent adoption as a logic made to appear as (...) if it exists prior to, and separate from, ethics and values. This logic, which was embraced in the Academy of Management from the 1960s, casts management researchers as ethical agents who ought to know about a reality conceptualized as naturally existing in the image of statistics and probability (replete with ‘constructs’), while overlooking that S&P logic and practices, which researchers made for themselves, have an appreciable role in making the world appear this way. We introduce a different way to conceptualize reality and ethics, wherein the process of scientific inquiry itself requires an examination of its own practices and commitments. Instead of resorting to decontextualized notions of ‘rigor’ and its ‘best practices,’ quantitative researchers can adopt more purposeful ways to reason about the ethics and relevance of their methods and their science. We end by considering implications for addressing ‘post truth’ and ‘alternative facts’ problems as collective concerns, wherein it is actually the pluralistic nature of description that makes defending a collectively valuable version of reality so important and urgent. (shrink)

The interventionist theory of causation has been advertised as an empirically informed and more nuanced approach to causality than the competing theories. However, previous literature has not yet analyzed the regression discontinuity (hereafter, RD) and the difference-in-differences (hereafter, DD) within an interventionist framework. In this paper, I point out several drawbacks of using the interventionist methodology for justifying the DD and RD designs. However, I argue that the first step towards enhancing our understanding of the DD and RD designs from (...) an interventionist perspective is to take advantage of the assumptions of common trend and continuity. (shrink)

Social scientists often draw on a variety of evidence for their causal inferences. There is also a call to use a greater variety of evidence in social science research. This topical collection examines the philosophical foundations and implications of evidential diversity in the social sciences. It assesses the application of Evidential Pluralism in the context of the social sciences, especially its application to economics and political science. It also discusses the concept of causation in cognitive science and the implications of (...) evidential diversity for the social sciences. (shrink)

Evidential Pluralism maintains that in order to establish a causal claim one normally needs to establish the existence of an appropriate conditional correlation and the existence of an appropriate mechanism complex, so when assessing a causal claim one ought to consider both association studies and mechanistic studies. Hitherto, Evidential Pluralism has been applied to medicine, leading to the EBM+ programme, which recommends that evidence-based medicine should systematically evaluate mechanistic studies alongside clinical studies. This paper argues that Evidential Pluralism can also (...) be fruitfully applied to the social sciences. In particular, Evidential Pluralism provides (i) a new approach to evidence-based policy; (ii) an account of the evidential relationships in more theoretical research; and (iii) new philosophical motivation for mixed methods research. The application of Evidential Pluralism to the social sciences is also defended against two objections. (shrink)

Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to accredited positions in the philosophy of causality and in social science methodology, invariance under intervention provides the most reliable test to decide whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal (...) methodology; consequently, the argument goes, the account is ill-suited to those contexts where manipulations are not performed, for instance, the social sciences. The article aims to extend the account of invariance (under intervention), in a way that manipulations on the putative causal factors are not methodologically fundamental, and yet invariance remains key for causal assessment both in experimental and non-experimental contexts. (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)

Evidential pluralists, like Federica Russo and Jon Williamson, argue that causal claims should be corroborated by establishing both the existence of a suitable correlation and a suitable mechanism complex. At first glance, this fits well with mixed method research in the social sciences, which often involves a pluralist combination of statistical and mechanistic evidence. However, statistical evidence concerns a population of cases, while mechanistic evidence is found in individual case studies. How should researchers combine such general statistical evidence and specific (...) mechanistic evidence? This article discusses a very recent answer to this question, ‘multi-method large-N qualitative analysis’ or multi-method LNQA, popular in political science and international relations studies of rare events like democratic transitions and cease-fire agreements. Multi-method LNQA combines a comprehensive study of all relevant event cases with statistical analysis, in an attempt to solve the issues of generalization faced by other types of qualitative research, such as selection bias and lack of representativeness. I will argue that the kind of general causal claim that multi-method LNQA is after, however, is crucially different from the average treatment effect found in statistical analysis and can in fact only be supported with mechanistic evidence. I conclude from this that mixed method research, and thereby evidential pluralism, may be inappropriate in this context. (shrink)

Evidential pluralism has been used to justify mixed-method research in political science. The combination of methodologies within case study analysis, however, has not received as much attention. This article applies the theory of evidential pluralism to causal inference in the case study method process tracing. I argue that different methodologies for process tracing commit to distinct fundamental theories of causation. I show that, problematically, one methodology may not recognize as genuine knowledge the fundamental claims of the other. By evaluating the (...) epistemic reliability of these fundamental claims, we can find a way out of such conflicts and rescue pluralism. (shrink)

Philosophers of social science have emphasized mechanistic approaches to causal inquiry for some time now, showing why focusing on the mechanisms behind correlations is preferable to focusing on correlations alone (cf. Johnson 2006, Little 1991, Reiss 2007, 2009, Steel 2004, see also King, Keohane, and Verba 1994 for an example of purely correlational research). In Process Tracing: from Metaphor to Analytic Tool, political scientists Andrew Bennett and Jeffrey Checkel present a concrete method for finding evidence of causal mechanisms, process tracing. (...) Process tracers give evidence for causal relations in terms of the observable implications of the underlying causal mechanisms through which a putative cause affects some effect of interest. Such observable implications often take the form of a chain of events, or process, which connects cause and effect. Though Process Tracing contains applications of mechanistic reasoning unfamiliar to philosophers, and as such will be of interest to those working in the mechanist tradition, Bennett and Checkel’s own discussion of the philosophical foundations of process tracing is limited. The reader will have to delve deep into the volume’s contributed chapters for links between the literature on causation and this new method. (shrink)

I present an alternative account of causation in the biomedical and social sciences according to which the meaning of causal claims is given by their inferential relations to other claims. Specifically, I will argue that causal claims are inferentially related to certain evidential claims as well as claims about explanation, prediction, intervention and responsibility. I explain in some detail what it means for a claim to be inferentially related to another and finally derive some implication of the proposed account for (...) the epistemology, semantics and metaphysics of causation. (shrink)

Two approaches to evidential reasoning compete in the biomedical and social sciences: the experimental and the pragmatist. Whereas experimentalism has received considerable philosophical analysis and support since the times of Bacon and Mill, pragmatism about evidence has been neither articulated nor defended. The overall aim is to fill this gap and develop a theory that articulates the latter. The main ideas of the theory will be illustrated and supported by a case study on the smoking/lung cancer controversy in the 1950s.

Pre-emption cases have been taken by almost everyone to imply the unviability of the simple counterfactual theory of causation. Yet there is ample motivation from scientific practice to endorse a simple version of the theory if we can. There is a way in which a simple counterfactual theory, at least if understood contrastively, can be supported even while acknowledging that intuition goes firmly against it in pre-emption cases—or rather, only in some of those cases. For I present several new pre-emption (...) cases in which causal intuition does not go against the counterfactual theory, a fact that has been verified experimentally. I suggest an account of framing effects that can square the circle. Crucially, this account offers hope of theoretical salvation—but only to the counterfactual theory of causation, not to others. Again, there is (admittedly only preliminary) experimental support for this account. (shrink)

In his recent paper in the Journal of Economic Methodology, Tobias Henschen puts forth a manipulationist definition of macroeconomic causality that strives for adequacy. As the notion of ‘adequacy’...

The results of econometric modeling are fragile in the sense that minor changes in estimation techniques or sample can lead to statistical models that support inconsistent causal hypotheses. The fragility of econometric results undermines making conclusive inferences from the empirical literature. I argue that the program of evidential pluralism, which originated in the context of medicine and encapsulates to the normative reading of the Russo-Williamson Thesis that causal claims need the support of both difference-making and mechanistic evidence, offers a ground (...) for resolving empirical disagreements. I analyze a recent econometric controversy regarding the tax elasticity of cigarette consumption and smoking intensity. Both studies apply plausible estimation techniques but report inconsistent results. I show that mechanistic evidence allows for discriminating econometric models representing genuine causal relations from accidental dependencies in data. Furthermore, I discuss the differences between biological and social mechanisms and mechanistic evidence across the disciplines. I show that economists mainly rely on mathematical models to represent possible mechanisms. Still, claiming the actuality of the represented mechanisms requires establishing that crucial assumptions of these models are descriptively adequate. I exemplify my approach to assessing the quality of mechanistic evidence in economics with an analysis of two models of rational addiction. (shrink)

The Russo Williamson thesis (RWT) states that a causal claim can be established only if it can be established that there is a difference-making relationship between the cause and the effect, and that there is a mechanism linking the cause and the effect that is responsible for such a difference-making relationship (Russo & Williamson, 2007). The applicability of Russo and Williamson’s idea was hugely debated in relation to biomedical research, and recently it has been applied to the social sciences (Shan (...) & Williamson, 2021). While many philosophers and social scientists have advocated the use of different kinds of evidence for causal discoveries, others have criticised this approach. With this paper, I aim to defend RWT from criticisms and to show its importance in the social sciences. The paper is structured as follows. After a brief introduction, in Sect. 2, I will summarise RWT, and in Sect. 3 I will describe how this approach can be applied to the social sciences. In Sect. 4, I will reconstruct two main criticisms of this thesis proposed in the philosophy of the social sciences literature: namely (i) RWT is not used in the social sciences, (ii) RWT does not work. For each criticism I will provide a defence of RWT. My defence will be based on two general considerations: (i) RWT appears perfectly in line with the research methods used in the social sciences and (ii) RWT can be applied successfully to establish causal claims. In Sect. 5, moreover, I will examine the causal accounts that have motivated such criticisms and I will argue that they should be rejected to endorse RWT and a causal account able to accommodate the current use of mechanistic and difference-making evidence in the social sciences. Section 6 will conclude with a note on the relevance of RWT in both its descriptive and normative form. (shrink)

The ‘value-free ideal’ has been called into question for several reasons. It does not include “epistemic values”—viewed as characteristic of ‘good science’—and rejects the so-called ‘contextual’, ‘non-cognitive’ or ‘non-epistemic’ values—all of them personal, moral, or political values. This paper analyzes a possible complementary argument about the dubitable validity of the value-free ideal, specifically focusing on social sciences, with a two-fold strategy. First, it will consider that values are natural facts in a broad or ‘liberal naturalist’ sense and, thus, a legitimate (...) part of those sciences. Second, the paper will not reject the value-free ideal; rather, it will construe this ideal in a special way, not casting values aside in sciences, but bringing them to the table and rationally discussing them. Today’s predominant naturalistic view has tended to ‘naturalize’ values by looking for physicalist explanations for them—a move resisted by defenders of normativism in social sciences. At the same time, a contending ‘liberal naturalist’ stream has emerged, claiming that not all natural entities can be explained by the methods and concepts of physical sciences, and favors a non-materialist naturalism which includes mind, consciousness, meaning and value as fundamental parts of nature that cannot be reduced to matter. Hence, it may be posited that non-epistemic values could be ‘naturally’ included in the field of human sciences. (shrink)

Most contemporary political science researchers are advocates of multimethod research, however, the value and proper role of qualitative methodologies, like case study analysis, is disputed. A pluralistic philosophy of science can shed light on this debate. Methodological pluralism is indeed valuable, but does not entail causal pluralism. Pluralism about the goals of science is relevant to the debate and suggests a focus on the difference between evidence for warrant and evidence for use. I propose that case study research provides evidence (...) for use through providing information that bears on the applicability of causal generalizations and risk assessment. (shrink)

This article examines two theses formulated by Russo and Williamson in their study of causal inference in the health sciences. The two theses are assessed against evidence from a specific case in the social sciences, i.e., research on the institutional determinants of the aggregate unemployment rate. The first Russo–Williamson Thesis is that a causal claim can only be established when it is jointly supported by difference-making and mechanistic evidence. This thesis is shown not to hold. While researchers in my case (...) study draw extensively on both types of evidence, one causal claim out of the three analyzed is established even though it is exclusively supported by mechanistic evidence. The second Russo–Williamson Thesis is that standard accounts of causality fail to handle the dualist epistemology highlighted in the first Thesis. I argue that a counterfactual-manipulationist account of causality—which is endorsed by many philosophers as well as many social scientists—can perfectly make sense of the typical strategy in my case study to draw on both difference-making and mechanistic evidence; it is just an instance of the common strategy of increasing evidential variety. (shrink)

How many notions of cause are there? The causality literature is witnessing a flourishing of pluralist positions. Here I focus on a recent debate on whether interpreting causality in terms of inferential relations commits one to semantic pluralism (Reiss 2011) or not (Williamson 2006). I argue that inferentialism is compatible with a ‘weak’ form of monism, where causality is envisaged as one, vague cluster concept. I offer two arguments for this, one for vagueness, one for uniqueness. Finally, I qualify in (...) what sense the resulting form of monism is ‘weak’.¿Cuántas nociones de causa hay? Hay un resurgir de posiciones pluralistas en la bibliografía sobre causalidad. Me ocuparé aquí de un debate reciente sobre si la interpretación de la causalidad en términos de relaciones inferenciales nos compromete con el pluralismo semántico (Reiss 2011) o no (Williamson 2006). Defiendo que el inferencialismo es compatible con una forma ’debil’ de monismo, donde la causalidad se presenta como un único concepto vago y de agrupación (cluster). Ofrezco para ello dos argumentos, uno sobre vaguedad y otro sobre unicidad. Finalmente, matizaré en qué sentido esta forma demonismo es ’débil’. (shrink)

Many ecological economists have argued that some natural capital should be preserved for posterity. Yet, among environmental philosophers, the preservation paradox entails that preserving parts of nature, including those denoted by natural capital, is impossible. The paradox claims that nature is a realm of phenomena independent of intentional human agency, that preserving and restoring nature require intentional human agency, and, therefore, no one can preserve or restore nature (without making it artificial). While this article argues that the preservation paradox is (...) more difficult to resolve than ordinarily recognized, it also concludes by sketching a positive way to understand what it means to preserve natural capital during the Anthropocene. (shrink)

In its most recent form, the debate about the relationship between quantitative and qualitative methodology in political science has been shaped by the publication of Designing Social Inquiry: Scientific Inference in Qualitative Research by Gary King, Robert O. Keohane, and Sidney Verba in 1994 (hereafter DSI). The focus of this debate has been case study research. DSI advocates that qualitative research, particularly case study research, be modeled on the template of quantitative research. The authors claim that all research has the (...) same logic of inquiry and that this is most clearly exemplified in quantitative work. I argue that the underlying philosophy of science of DSI is monistic and positivistic in ways not productive for understanding various different purposes that political science knowledge may have. Different methodologies have different strengths and so are suited to different ends. I examine this in relation to Julian Reiss’s discussion of different concepts of causality and argue that case study research is suited to understanding causal mechanisms in ways that make such research better suited to inform policy decisions. I finish with an example using David Fearon’s 2006 Congressional Testimony on Iraq. (shrink)

According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...) explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation (pp. 331–496). New York: Free Press; Kitcher (1989); Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25). My concern is with the minimal suggestion that an adequate philosophical theory of scientific explanation can limit its attention to the format or structure with which theories are represented. The representational subsumption view is a plausible hypothesis about the psychology of understanding. It is also a plausible claim about how scientists present their knowledge to the world. However, one cannot address the central questions for a philosophical theory of scientific explanation without turning one’s attention from the structure of representations to the basic commitments about the worldly structures that plausibly count as explanatory. A philosophical theory of scientific explanation should achieve two goals. The first is explanatory demarcation. It should show how explanation relates with other scientific achievements, such as control, description, measurement, prediction, and taxonomy. The second is explanatory normativity. It should say when putative explanations succeed and fail. One cannot achieve these goals without undertaking commitments about the kinds of ontic structures that plausibly count as explanatory. Representations convey explanatory information about a phenomenon when and only when they describe the ontic explanations for those phenomena. (shrink)

The notion of ‘causal web’ emerged in the epidemiological literature in the early Sixties and had to wait until the Nineties for a thorough critical appraisal. Famously, Nancy Krieger argued that such a notion isn’t helpful unless we specify what kind of spiders create the webs. This means, according to Krieger, (i) that the role of the spiders is to provide an explanation of the yarns of the web and (ii) that the sought spiders have to be biological and social. (...) This paper contributes to the development of the notion of causal web, elaborating on the two following points: (i) to catch the spiders we need multi-fold evidence—specifically, mechanistic and difference-making—and (ii) for the eco-social to be explanatory, the web has to be mechanistic in a sense to be specified. (shrink)