Philosophers of science typically associate the causal‐mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon’s account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex‐systems approach avoids certain (...) objections to Salmon’s account of causal‐mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

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 (...) plurality of evidence. We discuss the consequences of ‘evidential pluralism’ in the context of econometric modelling. (shrink)

Probability is a guide to life partly because it is a guide to causality. Work over the last two decades using Bayes nets supposes that probability is a very sure guide to causality. I think not, and I shall argue that here. Almost all the objections I list are well-known. But I have come to see them in a different light by reflecting again on the original work in this area by Wolfgang Spohn and his recent defense of it in (...) a paper titled “Bayesian Nets Are All There Is to Causality”. (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.

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

It has been the dominant view that probabilistic explanations of particular facts must be inductive in character. I argue here that this view is mistaken, and that the aim of probabilistic explanation is not to demonstrate that the explanandum fact was nomically expectable, but to give an account of the chance mechanism(s) responsible for it. To this end, a deductive-nomological model of probabilistic explanation is developed and defended. Such a model has application only when the probabilities occurring in covering laws (...) can be interpreted as measures of objective chance, expressing the strength of physical propensities. Unlike inductive models of probabilistic explanation, this deductive model stands in no need of troublesome requirements of maximal specificity or epistemic relativization. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...) a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

The paper analyzes the methods that macroeconomists can use to provide evidence in support of causal hypotheses: the instrumental variable method and econometric causality tests. It argues that the evidence that macroeconomists provide when using these methods is in principle too inconclusive to support the hypothesis that X directly type-level causes Y, where X and Y stand for macroeconomic aggregates like the real interest rate and aggregate demand. The evidence provided by the IV method is too inconclusive because it derives (...) from conditions requiring that there be no confounders of I and X and X and Y, and because in macroeconomics, confounders that cannot be controlled for or measured are likely to be present. The evidence provided by econometric causality tests is too inconclusive because they can be shown to rely on the conditions of the IV method at least tacitly. (shrink)

This paper examines Patrick Suppes’ probabilistic theory of causality understood as a theory of causal inference, and draws some lessons for empirical economics and contemporary debates in the foundations of econometrics. It argues that a standard method of empirical economics, multiple regression, is inadequate for most but the simplest applications, that the Bayes’ nets approach, which can be understood as a generalisation of Suppes’ theory, constitutes a considerable improvement but is still subject to important limitations, and that the currently fashionable (...) ‘design-based approach’ suffers from the same flaws Suppes anticipated a long time ago. It then sketches an alternative in response, one that differs drastically from the formalisms Suppes endorsed but is consistent with his pragmatic general take on science. (shrink)

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

:Proponents of behavioural policies seek to justify them as ‘evidence-based’. Yet they typically fail to show through which mechanisms these policies operate. This paper shows – at the hand of examples from economics and psychology – that without sufficient mechanistic evidence, one often cannot determine whether a given policy in its target environment will be effective, robust, persistent or welfare-improving. Because these properties are important for justification, policies that lack sufficient support from mechanistic evidence should not be called ‘evidence-based’.

A recent movement in the social sciences and philosophy of the social sciences focuses on mechanisms as a central analytical unit. Starting from a pluralist perspective on the aims of the social sciences, I argue that there are a number of important aims to which knowledge about mechanisms—whatever their virtues relative to other aims—contributes very little at best and that investigating mechanisms is therefore a methodological strategy with fairly limited applicability. Key Words: social science • mechanisms • explanation • critical (...) realism • methodology. (shrink)

:The most basic question one can ask of a model is ‘What is the effect on variable y2 of variable y1?’ Causation is ‘implementation neutral’ when all interventions on external variables that lead to a given change in y1 have the same effect on y2, so that the effect of y1 on y2 is defined unambiguously. Familiar ideas of causal analysis do not apply when causation is implementation neutral. For example, a cause variable cannot be linked to an effect variable (...) by both a direct path and a distinct indirect path. Discussion of empirical aspects of implementation neutrality leads to further unexpected results, such as that if one variable causes another the coefficient representing that causal link is always identified. (shrink)

showing what makes causal facts both true and accessible enough for us to have the knowledge of them that we ordinarily take ourselves to have. Some current approaches to analyzing causation were once resisted. First, analyses that use the counterfactual conditional were viewed with suspicion because philosophers also sought (and still do seek) similar understanding of counterfactual facts. Since the same can be said for the other nomic concepts--causation, lawhood, explanation, chance, dispositions, and their conceptual kin--philosophy demonstrated a preference for (...) non-nomic definitions of causation, analytic completions of (S) with no nomic terms in the analysans. Recently, however, philosophers have been less demanding regarding what terms may be used. Attention has been given to analyzing causation in terms of chance, the counterfactual conditional, and lawhood. If we reserve the term ‘causal’ for the terms and concepts that have extremely obvious connections.. (shrink)

Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interpreted in a way that does not depend on it. Nevertheless, he shows that this more charitable version of the account is (...) still unsuccessful as it stands. Consequently, he advances a proposal for shoring up the account, which is founded on the possibility of acquiring knowledge of social mechanisms by linking together norms or practices found in a society. Key Words: causality • social mechanisms • interpretation • anthropology. (shrink)

ABSTRACTIn his recent book, Rodrik [. Economics rules. Why economics works, when it fails, and how to tell the difference. Oxford University Press] proposes an account of model pluralism according to which multiple models of the same target are acceptable as long as one model is more useful for one purpose and another is more useful for another purpose. How, then, is the right model for the purpose selected? Rodrik roughly outlines a selection procedure, which we formalize to enhance understanding (...) of his account of model pluralism and to advance the critical discussion. (shrink)