Julian Reiss

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