Opponents of ceteris paribus laws are apt to complain that the laws are vague and untestable. Indeed, claims to this effect are made by Earman, Roberts and Smith in this volume. I argue that these kinds of claims rely on too narrow a view about what kinds of concepts we can and do regularly use in successful sciences and on too optimistic a view about the extent of application of even our most successful non-ceteris paribus laws. When it comes to (...) testing, we test ceteris paribus laws in exactly the same way that we test laws without the ceteris paribus antecedent. But at least when the ceteris paribus antecedent is there we have an explicit acknowledgment of important procedures we must take in the design of the experiments — i.e., procedures to control for “all interferences” even those we cannot identify under the concepts of any known theory. (shrink)

Hunting Causes and Using Them argues that causation is not one thing, as commonly assumed, but many. There is a huge variety of causal relations, each with different characterizing features, different methods for discovery and different uses to which it can be put. In this collection of new and previously published essays, Nancy Cartwright provides a critical survey of philosophical and economic literature on causality, with a special focus on the currently fashionable Bayes-nets and invariance methods - and it exposes (...) a huge gap in that literature. Almost every account treats either exclusively how to hunt causes or how to use them. But where is the bridge between? It's no good knowing how to warrant a causal claim if we don't know what we can do with that claim once we have it. This book will interest philosophers, economists and social scientists. (shrink)

Studies causation both as a concept and as it is 'in the objects.' Offers new accounts of the logic of singular causal statements, the form of causal regularities, the detection of causal relationships, the asymmetry of cause and effect, and necessary connection, and it relates causation to functional and statistical laws and to teleology.

There has been no shortage of such conceptual analyses and no shortage of counterexamples to all of them. The counterexamples exploit, at least partly, situations in which we are presumed to have clear intuitions about what causes what, but which intuitions are not being respected by the suggested philosophical analysis. The counterexamples typically lead to a battery of sophisticated attempts to revise or amend the philosophical analysis so that it is saved from refutation. These attempts, typically, either deny the intuitions (...) on which the counterexamples are based or accommodate the problematic cases within the theory by adding further clauses to the original philosophical analysis. The result of all this is that where the original philosophical theory rested on a simple, forceful and intuitively plausible idea (e.g., that causation consists in a relation of counterfactual dependence between discrete events), the modified philosophical theory becomes very convoluted, somewhat ad hoc and implausible. (shrink)

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

I put forward several desiderata that a philosophical theory of causality should satisfy: it should account for the objectivity of causality, it should underpin formalisms for causal reasoning, it should admit a viable epistemology, it should be able to cope with the great variety of causal claims that are made, and it should be ontologically parsimonious. I argue that Nancy Cartwright’s dispositional account of causality goes part way towards meeting these criteria but is lacking in important respects. I go on (...) to argue that my epistemic account, which ties causal relationships to an agent’s knowledge and ignorance, performs well in the light of the desiderata. Such an account, I claim, is all we require from a theory of causality. (shrink)

It is tempting to analyse causality in terms of just one of the indicators of causal relationships, e.g., mechanisms, probabilistic dependencies or independencies, counterfactual conditionals or agency considerations. While such an analysis will surely shed light on some aspect of our concept of cause, it will fail to capture the whole, rather multifarious, notion. So one might instead plump for pluralism: a different analysis for a different occasion. But we do not seem to have lots of different concepts of cause (...) – just one eclectic notion. The resolution of this conundrum, I think, requires us to accept that our causal beliefs are generated by a wide variety of indicators, but to deny that this variety of indicators yields a variety of concepts of cause. This focus on the relation between evidence and causal beliefs leads to what I call epistemic causality. Under this view, certain causal beliefs are appropriate or rational on the basis of observed evidence; our notion of cause can be understood purely in terms of these rational beliefs. Causality, then, is a feature of our epistemic representation of the world, rather than of the world itself. This yields one, multifaceted notion of cause. (shrink)

How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justifiable as a default assumption. In which case the results of the inductive causal learning procedure must be tested before they can be accepted. This yields a synthesis of the hypothetico-deductive and inductive accounts, which forms (...) the focus of this paper. I discuss the justification of this synthesis and draw an analogy between objective Bayesianism and the account of causal learning presented here. (shrink)

This chapter addresses two questions: what are causal relationships? how can one discover causal relationships? I provide a survey of the principal answers given to these questions, followed by an introduction to my own view, epistemic causality, and then a comparison of epistemic causality with accounts provided by Judea Pearl and Huw Price.

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)

When two causally independent processes each have a quantity that increases monotonically (either deterministically or in probabilistic expectation), the two quantities will be correlated, thus providing a counterexample to Reichenbach's principle of the common cause. Several philosophers have denied this, but I argue that their efforts to save the principle are unsuccessful. Still, one salvage attempt does suggest a weaker principle that avoids the initial counterexample. However, even this weakened principle is mistaken, as can be seen by exploring the concepts (...) of homology and homoplasy used in evolutionary biology. I argue that the kernel of truth in the principle of the common cause is to be found by separating metaphysical and epistemological issues; as far as the epistemology is concerned, the Likelihood Principle is central. (shrink)

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory (...) of causality that unifies its mechanistic and probabilistic aspects. We argue that the epistemic theory of causality provides the required unification. (shrink)

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)

, whereby some causes are deemed more important than others, are ubiquitous in historical studies. Drawing from influential recent work on causation, I develop a definition of causal-explanatory strength. This makes clear exactly which aspects of explanatory weighting are subjective and which objective. It also sheds new light on several traditional issues, showing for instance that: underlying causes need not be more important than proximate ones; several different causes can each be responsible for most of an effect; small causes need (...) not be less important than big ones; and non-additive interactive effects between causes present no particular difficulty. Key Words: causation • explanation • history • interaction • proximate • underlying. (shrink)

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 don’t entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. Further, (...) I shall argue that this monistic epistemology is also involved in alternative philosophical theories of causation. (shrink)

This is a review of Marc Lange's _Natural Laws in Scientific Practice<D>.

It is often presumed that the laws of nature have special significance for scientific reasoning. But the laws' distinctive roles have proven notoriously difficult to identify--leading some philosophers to question if they hold such roles at all. This study offers original accounts of the roles that natural laws play in connection with counterfactual conditionals, inductive projections, and scientific explanations, and of what the laws must be in order for them to be capable of playing these roles. Particular attention is given (...) to laws of special sciences, levels of scientific explanation, natural kinds, ceteris-paribus clauses, and physically necessary non-laws. (shrink)

There are many ways of attaching two objects together: for example, they can be connected, linked, tied or bound together; and the connection, link, tie or bind can be made of chain, rope, or cement. Every one of these binding methods has been used as a metaphor for causation. What is the real significance of these metaphors? They express a commitment to a certain way of thinking about causation, summarized in the following thesis: ‘In any concrete situation, there is an (...) objective fact of the matter as to whether two events are in fact bound by the causal relation. It is the aim of philosophical inquiry to analyze this objective relation.’ Through a variety of examples, I hope to cast doubt on this seemingly innocuous thesis. The problem lies not with the word ‘objective’, but with the word ‘the’. The goal of a philosophical account of causation should not be to capture the causal relation, but rather to capture the many ways in which the events of the world can be bound together. 1 The metaphors 2 Unpacking the metaphors 3 Theories of causation 4 The two assassins 5 The birth control pills 6 The smoker-protector gene 7 The bicycle thief 8 Further examples 8.1 Indeterminism 8.2 Probability-lowering causes 8.3 Parts vs wholes 8.4 Symmetric overdetermination 8.5 Delayers 8.6 Causation by omission 8.7 Double prevention/disconnection 8.8 Preemptive prevention 8.9 Quantitative variables 9 Conclusion. (shrink)

An updated and extended second edition supporting the findings of its well-known predecessor which claimed that courts employ common-sense notions of causation in determining legal responsibility.

Causal pluralism is currently a hot topic in philosophy. However, the consequences of this view on causation for scientific knowledge and scientific methodology are heavily underexposed in the present debate. My aim in this paper is to argue that an epistemological-methodological point of view should be valued as a line of approach on its own and to demonstrate how epistemological- methodological causal pluralism differs in its scope from conceptual and metaphysical causal pluralism. Further, I defend epistemological-methodological causal pluralism and try (...) to illustrate that scientific practice needs diverse causal concepts in diverse domains, and even diverse causal concepts within singular domains. (shrink)

This work uses counterfactual conjectures to draw inferences from historical data. It proposes criteria for evaluating counterfactuals that occur in diverse methodological contexts including: comparative case studies; game theory; and statistical analysis.

Causality in Macroeconomics examines causality while taking macroeconomics seriously. A pragmatic and realistic philosophy is joined to a macroeconomic foundation that refines Herbert Simon's well-known work on causal order to make a case for a structural approach to causality. The structural approach is used to understand modern rational expectations models, regime switching models, Granger causality, vector autoregressions, the Lucas critique, and concept exogeneity. Techniques of causal inference based on patterns of stability and instability in the face of identified regime changes (...) are developed and illustrated in two empirical case studies. (shrink)

This book is an expanded and revised edition of the author's critically acclaimed volume Nuts and Bolts for the Social Sciences. In twenty-six succinct chapters, Jon Elster provides an account of the nature of explanation in the social sciences. He offers an overview of key explanatory mechanisms in the social sciences, relying on hundreds of examples and drawing on a large variety of sources - psychology, behavioral economics, biology, political science, historical writings, philosophy and fiction. Written in accessible and jargon-free (...) language, Elster aims at accuracy and clarity while eschewing formal models. In a provocative conclusion, Elster defends the centrality of qualitative social sciences in a two-front war against soft and hard forms of obscurantism. (shrink)

"This is a wonderful book! In "How Scientists Explain Disease," Paul Thagard offers us a delightful essay combining science, its history, philosophy, and sociology.

Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, brings together two important research topics: how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.

In this book, J. L. Mackie makes a careful study of several philosophical issues involved in his account of causation. Mackie follows Hume's distinction between causation as a concept and causation as it is ‘in the objects’ and attempts to provide an account of both aspects. Mackie examines the treatment of causation by philosophers such as Hume, Kant, Mill, Russell, Ducasse, Kneale, Hart and Honore, and von Wright. Mackie's own account involves an analysis of causal statements in terms of counterfactual (...) conditionals though these are judged to be incapable of giving a complete account of causation. Mackie argues that regularity theory too can only offer an incomplete picture of the nature of causation. In the course of his analysis, Mackie critically examines the account of causation offered by Kant, as well as the contemporary Kantian approaches offered by philosophers such as Bennett and Strawson. Also addressed are issues such as the direction of causation, the relation of statistical laws and functional laws, the role of causal statements in legal contexts, and the understanding of causes both as ‘facts’ and ‘events’. Throughout the discussion of these topics, Mackie develops his own complex account of the nature of causation, finally bringing his analysis to bear in regard to the topic of teleology and the question of whether final causes can be justifiably reduced to efficient causes. (shrink)