Julian Reiss

This encyclopedia is the first of its kind in bringing together philosophy and the social sciences. It is not only about the philosophy of the social sciences but, going beyond that, it is also about the relationship between philosophy and the social sciences. The subject of this encyclopedia is purposefully multi- and inter-disciplinary. Knowledge boundaries are both delineated and crossed over. The goal is to convey a clear sense of how philosophy looks at the social sciences and to mark out a detailed picture of how the two are interrelated: interwoven at certain times but also differentiated and contrasted at others. The Entries cover topics of central significance but also those that are both controversial and on the cutting-edge, underlining the unique mark of this Encyclopedia: the interrelationship between philosophy and the social sciences, especially as it is found in fresh ideas and unprecedented hybrid disciplinary areas. The Encyclopedia serves a further dual purpose: it contributes to the renewal of the philosophy of the social sciences and helps to promote novel modes of thinking about some of its classic problems.

In the human sciences, experimental research is used to establish causal relationships. However, the extrapolation of these results to the target population can be problematic. To facilitate extrapolation, we propose to use the statistical technique Latent Class Regression Analysis in combination with the analogical reasoning theory for extrapolation. This statistical technique can identify latent classes that differ in the effect of X on Y. In order to extrapolate by means of analogical reasoning, one can characterize the latent classes by a combination of features, and then compare these features to features of the target.

Francesco Guala once wrote that ‘The problem of extrapolation is a minor scandal in the philosophy of science’. This paper agrees with the statement, but for reasons different from Guala’s. The scandal is not, or not any longer, that the problem has been ignored in the philosophy of science. The scandal is that framing the problem as one of external validity encourages poor evidential reasoning. The aim of this paper is to propose an alternative—an alternative which constitutes much better evidential reasoning about target systems of interest, and which makes do without consideration of external validity.

One way to make philosophy of science more socially relevant is to attend to specific scientific practises that affect society to a great extent. One such practise is biomedical research. This paper looks at contemporary U.S. biomedical research in particular and argues that it suffers from important epistemic, moral and socioeconomic failings. It then discusses and criticises existing approaches to improve on the status quo, most prominently by Thomas Pogge (a political philosopher), Joseph Stiglitz (a Nobel-prize winning economist) and James Robert Brown (a philosopher of science). Finally, it sketches an alternative proposal and argues for its superiority. The proposal has four components: changing the intellectual property regime; instituting independent clinical research; aligning innovators' and patients' interests; and enacting additional regulation

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.

In this book, Reiss argues in favor of a tight fit between evidence, concept and purpose in our causal investigations in the sciences. There is no doubt that the sciences employ a vast array of techniques to address causal questions such as controlled experiments, randomized trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and to the causal inquiry at hand? Reiss argues that there is no "gold standard" in settling causal issues against which other methods can be measured. Rather, the various methods of inference tend to be good only relative to certain interpretations of the word "cause", and each interpretation, in turn, helps to address some salient purpose (prediction, explanation or policy analysis) but not others. The main objective of this book is to explore the metaphysical and methodological consequences of this view in the context of numerous cases studies from the natural and social sciences.

This paper looks at an appeal to the authority of biomedical research that has recently been used by empirical economists to motivate and justify their methods. I argue that those who make this appeal mistake the nature of biomedical research. Randomised trials, which are said to have revolutionised biomedical research, are a central methodology, but according to only one paradigm. There is another paradigm at work in biomedical research, the inferentialist paradigm, in which randomised trials play no special role. I outline the inferentialist alternative in broad strokes, apply it to a recent controversy in econometrics and draw some general conclusions concerning econometric methodology

What is the correct concept behind measures of inflation? Does money cause business activity or is it the other way around? Shall we stimulate growth by raising aggregate demand or rather by lowering taxes and thereby providing incentives to produce? Policy-relevant questions such as these are of immediate and obvious importance to the welfare of societies. The standard approach in dealing with them is to build a model, based on economic theory, answer the question for the model world and then apply the results to economic phenomena outside. Data come in, if at all, only in testing a limited number of the model's consequences. Despite some critical voices, economic methodology too has by and large subscribed to a "theory first" approach to applied economics. Error in Economics systematically develops an alternative to the theory-based orthodoxy. It places the methodical study of evidence at the centre of the scientific enterprise and thus provides a foundation for a methodology of evidence-based economics. But the book does not stop at the truism that claims should be based on the best available evidence. Rather, detailed studies in the areas of measurement, causal inference and policy analysis show what it means for a claim to be evidence-based in the context of a concrete case. The examples discussed concern topics as diverse as consumer price indices, radio spectrum auctions, the transmission mechanism, natural experiments on minimum wages and the evaluation of counterfactuals for policy. Error in Economics is essential reading for economic methodologists, philosophers of science and anyone interested in how claims about socio-economic matters are validated.

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

Philosophy of Economics: A Contemporary Introduction is the first systematic textbook in the philosophy of economics. It introduces the epistemological, metaphysical and ethical problems that arise in economics, and presents detailed discussions of the solutions that have been offered. Throughout, philosophical issues are illustrated by and analysed in the context of concrete cases drawn from contemporary economics, the history of economic ideas, and actual economic events. This demonstrates the relevance of philosophy of economics both for the science of economics and for the economy. This text will provide an excellent introduction to the philosophy of economics for students and interested general readers alike

Explaining socio-economic phenomena is one important aim of economics. There is very little agreement, however, on what precisely constitutes an adequate economic explanation. Starting from the very influential but defective ‘deductive-nomological model’ of explanation, this article describes and criticizes the major contemporary competitors for such an account (the probabilistic–causal, the mechanistic–causal and the unificationist models) and argues that none of them can by itself capture all aspects of a good explanation. When seeking to explain a socio-economic phenomenon it should therefore be borne in mind that different types of explanation serve different purposes

The aim of this paper is to introduce the instrumental variables technique to the discussion about causal inference in econometrics. I show that it may lead to causally incorrect conclusions unless some fairly strong causal background assumptions are made, assumptions which are usually left implicit by econometricians. These assumptions are very similar to, albeit not identical with, James Woodward's definition of an ‘intervention’. I discuss similarities and differences of the two points of view and argue that—understood as a practical method of causal inference—the set presented here is superior.

I respond to some challenges raised by my critics. In particular, I argue in favour of six claims. First, against Alexandrova and Northcott, I point out that to deny the explanatoriness of economic models by assuming an ontic (specifically, causal) conception of explanation is to beg the question. Second, against defences of causal realism (by Hausman, Mäki, Rol and Grüne-Yanoff) I point out that they have provided no criterion to distinguish those claims a model makes that can be interpreted realistically (the model's ?causal content? or claims it makes about causal powers or mechanisms) and those the realist can safely ignore. Third, I point out that Hausman's and Rol's claims about robustness plus the empirical observation that economic models are hardly ever robust to the right kinds of specification changes imply that these models do not explain (which is problematic as my original article argued). Fourth, I point out that Sugden's response still leaves an important question unanswered, viz. what makes economic models explanatory. Fifth, I sketch an alternative, instrumentalist account of explanation and argue that it would fit Sugden's bill. Sixth, I point out that under this account of explanation, economic models would come out as non-explanatory, which brings us back to Alexandrova and Northcott's account and its associated difficulties. The ?explanation paradox?, therefore, remains unscathed

In this book, Reiss argues in favour of a tight fit between evidence, concept and purpose in our causal investigations in the sciences. There is no doubt that the sciences employ a vast array of techniques to address causal questions such as controlled experiments, randomized trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and to the causal inquiry at hand? Reiss argues that there is no "gold standard" in settling causal issues against which other methods can be measured. Rather, the various methods of inference tend to be good only relative to certain interpretations of the word "cause", and each interpretation, in turn, helps to address some salient purpose (prediction, explanation or policy analysis) but not others. The main objective of this book is to explore the metaphysical and methodological consequences of this view in the context of numerous cases studies from the natural and social sciences

This is the first instalment of a two-part paper on the counterfactual theory of causation. It is well known that this theory is ridden with counterexamples. Specifically, the following four features of the theory suffer from problems: it understands causation as a relation between events; counterfactual dependence is understood using a metric of similarity among possible worlds; it defines a non-discriminatory concept of causation; and it understands causation as transitive. A number of philosophers have recently proposed that causation is contrastive because making contrasts explicit defuses counterexamples. A contrastive causal claim has the following form: C rather than C* causes E rather than E*, where C* and E* are alternative or contrast events. In this paper, I show that making contrasts explicit does indeed defuse some counterexamples. However, I also argue that the examples discussed in the literature all share a common feature, viz. that the original causal judgement is ambiguous in one way or another. Contrasting does not help with counterexamples that do not have this feature. Part II of this paper then takes up the hard cases

This paper reviews all major theories of evidence such as the Bayesian theory, hypothetico-deductivism, satisfaction theories, error-statistics, Achinstein's explanationist theory and Cartwright's argument theory. All these theories fail to take adequate account of the context in which a hypothesis is established and used. It is argued that the context of an inquiry determines important facts about what evidence is, and how much and what kind has to be collected to establish a hypothesis for a given purpose.

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before.