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The purpose of this essay is to defend the deductive-nomological model of explanation against a number of criticisms that have been made of it. It has traditionally been thought that scientific explanations were causal and that scientific explanations involved deduction from laws. In recent years, however, this three-fold identity has been challenged: there are, it is argued, causal explanations that are not scientific, scientific explanations that are not deductive, deductions from laws that are neither causal explanations nor scientific explanations, and (...) |
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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 (...) |
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In this essay several aspects of the concept ‘indicator’ are criticized, as well as the technical consequences of those misconceptions. The most relevant among them is the pretence of measuring the validity of an indicator, e. g. the degree of semantic correspondence between the indicator and the concept which it is supposes to refer to. |
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Causal modeling methods such as path analysis, used in the social and natural sciences, are also highly relevant to philosophical problems of probabilistic causation and statistical explanation. We show how these methods can be effectively used (1) to improve and extend Salmon's S-R basis for statistical explanation, and (2) to repair Cartwright's resolution of Simpson's paradox, clarifying the relationship between statistical and causal claims. |
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The experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the ‘quantitative revolution’ in the nineteenth century. One of the often valued features of social science experimentation is precisely the fact that there are clear methodological rules regarding hypothesis testing that come from the methods of the natural sciences and from the methodology of RCTs in the biomedical sciences, and that allow for the adjudication among contentious causal claims. We examine (...) |
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The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...) |
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Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. (...) |
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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 (...) |
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You are, I am sure, just as aware as I am that the operational nodes of a complex problem, the points at which it can be split open to yield nuggets of new insight or achieve lasting advances, often lie in tediously technical details perhaps incomprehensible to all but specialists in the matter and anyways totally lacking in the romance and easy excitement which attract the topic's dilettantes. I would like you to hold fast to this appreciation, for the concerns (...) |
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Recent studies on probabilistic causation and statistical explanation (Cartwright 1979; Salmon 1984), I believe, have opened up the possibility of a genuine unification between philosophical approaches and causal modeling (CM) in the social, behavioral and biological sciences (Wright 1934; Blalock 1964; Asher 1976). This unification rests on the statistical tools employed, the principle of common cause, the irreducibility of causation to probability or statistics, and the idea of causal process as a suitable framework for understanding causal relationships. The aim of (...) |
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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 (...) |
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This paper is a realist argument for the existence of “social objects”. Social objects, I argue, are the outcome states of a contingent causal process and in turn posses causal properties. This argument has consequences for what we can mean by realism and consequences for the development of a realist methodology. Realism should abandon the notion of natural necessity in favour of a view that the “real” nature of the social world is contingent and necessity is only revealed in outcome (...) |
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In recent years, realism?particularly critical realism?has become an important philosophical and methodological foundation for social science. A key feature is that of natural necessity, but this coexists alongside an acceptance of contingency in the social world. I argue in this paper that there cannot be any natural necessity in the social world, but rather the real nature of the social world is that it is contingent. This need not lead to an abandonment of realism, and indeed I argue that a (...) |
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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. |
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During the past decades several philosophers of science and social scientists have been interested in the problems of causation. Recently attention has been given to probabilistic causation in dichotomous causal systems. The paper uses the basic features of probabilistic causation to argue that the causal modeling approaches developed by such researchers as Blalock and Duncan can provide, when an additional assumption is added, adequate qualitative measures of one variableś causal influence upon another. Finally, some of the difficulties and issues involved (...) |
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This paper is an assessment of an attempt, by James Greeno, to measure the explanatory power of statistical theories by means of the notion of transmitted information (It). It is argued that It has certain features that are inappropriate in a measure of explanatory power. In particular, given a statistical theory T with explanans variables St and explanandum variables Mj, it is argued that no plausible measure of explanatory power should depend on the probability P(Si) of occurrence of initial conditions (...) |
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One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their function (...) |
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This paper argues against Papineau's claim that causal relations can be reduced to correlations and defends Cartwright's thesis that they can be nevertheless boot-strapped from them, given sufficiently rich causal background knowledge. |
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The computer’s effect on our understanding of causation has been enormous. By the mid‐1980s, philosophical and social‐scientific work on the topic had left us with (1) no reasonable reductive account of causation and (2) a class of statistical causal models tied to linear regression. At this time, computer scientists were attacking the problem of equipping robots with models of the external that included probabilistic portrayals of uncertainty. To solve the problem of efficiently storing such knowledge, they introduced Bayes Networks and (...) |
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The fact that causal laws in the social sciences are most realistically expressed as both multivariate and stochastic has a number of very important implications for indirect measurement and generalizability. It becomes difficult to link theoretical definitions of general constructs in a one-to-one relationship to research operations, with the result that there is conceptual slippage in both experimental and nonexperimental research. It is argued that problems of this nature can be approached by developing specific multivariate causal models that incorporate sources (...) |
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Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal claims are established on the basis of a (...) |
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This paper makes explicit and takes issue with the bizarre view, which is unfortunately prevalent among social scientists, that causal relations are features of models only. There are some good reasons to represent causal factors with independent variables. But the association between causes and independent variables is only a desideratum in model construction. It is not a criterion for judging which things are causes and which are effects. |
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La relectura del experimento de Milgram y de otras lecturas a las que me ha llevado la invitación a esta colaboración me ha provocado una serie de reflexiones que van más allá de un comentario exclusivamente centrado y escueto en dicho experimento social para extenderme sobre otros aspectos tanto metodológicos como sustantivos. Algunos de estas reflexiones se sitúan en el epicentro de controversias típicas de las ciencias sociales y de la sociología en particular. |
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Two kinds of causal inference rules which are widely used by social scientists are investigated. Two conceptions of causation also widely used are explicated — the INUS and probabilistic conceptions of causation. It is shown that the causal inference rules which link correlation, a kind of partial correlation, and a conception of causation areinvalid. It is concluded anew methodology is required for causal inference. |
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Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence dont entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. Further, (...) |
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Philosophers and scientists have maintained that causation, correlation, and partial correlation are essentially related. These views give rise to various rules of causal inference. This essay considers the claims of several philosophers and social scientists for causal systems with dichotomous variables. In section 2 important commonalities and differences are explicated among four major conceptions of correlation. In section 3 it is argued that whether correlation can serve as a measure of A's causal influence on B depends upon the conception of (...) |
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