In this sequence of philosophical essays about natural science, the author argues that fundamental explanatory laws, the deepest and most admired successes of modern physics, do not in fact describe regularities that exist in nature. Cartwright draws from many real-life examples to propound a novel distinction: that theoretical entities, and the complex and localized laws that describe them, can be interpreted realistically, but the simple unifying laws of basic theory cannot.

Ever since David Hume, empiricists have barred powers and capacities from nature. In this book Cartwright argues that capacities are essential in our scientific world, and, contrary to empiricist orthodoxy, that they can meet sufficiently strict demands for testability. Econometrics is one discipline where probabilities are used to measure causal capacities, and the technology of modern physics provides several examples of testing capacities (such as lasers). Cartwright concludes by applying the lessons of the book about capacities and probabilities to the (...) explanation of the role of causality in quantum mechanics. (shrink)

We currently have on offer a variety of different theories of causation. Many are strikingly good, providing detailed and plausible treatments of exemplary cases; and all suffer from clear counterexamples. I argue that, contra Hume and Kant, this is because causation is not a single, monolithic concept. There are different kinds of causal relations imbedded in different kinds of systems, readily described using thick causal concepts. Our causal theories pick out important and useful structures that fit some familiar cases—cases we (...) discover and ones we devise to fit. (shrink)

What kinds of evidence reliably support predictions of effectiveness for health and social care interventions? There is increasing reliance, not only for health care policy and practice but also for more general social and economic policy deliberation, on evidence that comes from studies whose basic logic is that of JS Mill's method of difference. These include randomized controlled trials, case–control studies, cohort studies, and some uses of causal Bayes nets and counterfactual-licensing models like ones commonly developed in econometrics. The topic (...) of this paper is the 'external validity' of causal conclusions from these kinds of studies. We shall argue two claims. Claim, negative: external validity is the wrong idea; claim, positive: 'capacities' are almost always the right idea, if there is a right idea to be had. If we are right about these claims, it makes big problems for policy decisions. Many advice guides for grading policy predictions give top grades to a proposed policy if it has two good Mill's-method-of difference studies that support it. But if capacities are to serve as the conduit for support from a method-of-difference study to an effectiveness prediction, much more evidence, and much different in kind, is required. We will illustrate the complexities involved with the case of multisystemic therapy, an internationally adopted intervention to try to diminish antisocial behaviour in young people. (shrink)

La autora presenta algunas criticas generales al proyecto de reducir las leyes causales a probabilidades. Además, muestra que las leyes causales son imprescindibles para poder diferenciar las strategias efectivas de las que no lo son y da un criterio para considerar cuando podemos deducir causalidad a través de datos estadísticos.

In “The Toolbox of Science” (1995) together with Towfic Shomar we advocated a form of instrumentalism about scientific theories. We separately developed this view further in a number of subsequent works. Steven French, James Ladyman, Otavio Bueno and Newton Da Costa (FLBD) have since written at least eight papers and a book criticising our work. Here we defend ourselves. First we explain what we mean in denying that models derive from theory – and why their failure to do so should (...) be lamented. Second we defend our use of the London model of superconductivity as an example. Third we point out both advantages and weaknesses of FLBD’s techniques in comparison to traditional Anglophone versions of the semantic conception. Fourth we show that FLBD’s version of the semantic conception has not been applied to our case study. We conclude by raising doubts about FLBD’s overall project. (shrink)

In their rich and intricate paper ‘Independence, Invariance, and the Causal Markov Condition’, Daniel Hausman and James Woodward ([1999]) put forward two independent theses, which they label ‘level invariance’ and ‘manipulability’, and they claim that, given a specific set of assumptions, manipulability implies the causal Markov condition. These claims are interesting and important, and this paper is devoted to commenting on them. With respect to level invariance, I argue that Hausman and Woodward's discussion is confusing because, as I point out, (...) they use different senses of ‘intervention’ and ‘invariance’ without saying so. I shall remark on these various uses and point out that the thesis is true in at least two versions. The second thesis, however, is not true. I argue that in their formulation, the manipulability thesis is patently false and that a modified version does not fare better. Furthermore, I think their proof that manipulability implies the causal Markov condition is not conclusive. In the deterministic case it is valid but vacuous, whereas it is invalid in the probabilistic case. 1 Introduction 2 Intervention, invariance and modularity 3 The causal Markov condition: CM1 and CM2 4 From MOD to the causal Markov condition and back 5 A second argument for CM2 6 The proof of the causal Markov condition for probabilistic causes 7 ‘Cartwright's objection’ defended 8 Metaphysical defenses of the causal Markov condition 9 Conclusion. (shrink)

Most of the regularities that get represented as ‘laws’ in our sciences arise from, and are to be found regularly associated with, the successful operation of a nomological machine. Reference to the nomological machine must be included in the cp-clause of a cp-law if the entire cp-claim is to be true. We agree, for example, ‘ceteris paribus aspirins cure headaches’, but insist that they can only do so when swallowed by someone with the right physiological makeup and a headache. Besides (...) providing a necessary condition on the truth of the cp-law claim, recognising the nomological machine has great practical importance. Referring to the nomological machine makes explicit where the regularities are to be found, which is of central importance to the use of cp-laws for prediction and manipulation. Equally important, bringing the nomological machine to the fore brings into focus the make-up of the machine—its parts, their powers and their arrangements—and its context case-by-case. (shrink)

Randomized controlled trials (RCTs) are widely taken as the gold standard for establishing causal conclusions. Ideally conducted they ensure that the treatment ‘causes’ the outcome—in the experiment. But where else? This is the venerable question of external validity. I point out that the question comes in two importantly different forms: Is the specific causal conclusion warranted by the experiment true in a target situation? What will be the result of implementing the treatment there? This paper explains how the probabilistic theory (...) of causality implies that RCTs can establish causal conclusions and thereby provides an account of what exactly that causal conclusion is. Clarifying the exact form of the conclusion shows just what is necessary for it to hold in a new setting and also how much more is needed to see what the actual outcome would be there were the treatment implemented. (shrink)

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)

How can philosophy of science be of more practical use? One thing we can do is provide practicable advice about how to determine when one empirical claim is relevant to the truth of another; i.e., about evidential relevance. This matters especially for evidence-based policy, where advice is thin—and misleading—about how to tell what counts as evidence for policy effectiveness. This paper argues that good efficacy results (as in randomized controlled trials), which are all the rage now, are only a very (...) small part of the story. To tell what facts are relevant for judging policy effectiveness, we need to construct causal scenarios about will happen when the policy is implemented. (shrink)

We call for a new philosophical conception of models in physics. Some standard conceptions take models to be useful approximations to theorems, that are the chief means to test theories. Hence the heuristics of model building is dictated by the requirements and practice of theory-testing. In this paper we argue that a theory-driven view of models can not account for common procedures used by scientists to model phenomena. We illustrate this thesis with a case study: the construction of one of (...) the first comprehensive model of superconductivity by London brothers in 1934. Instead of theory-driven view of models, we suggest a phenomenologically-driven one. (shrink)

This paper argues that even when simple analogue models picture parallel worlds, they generally still serve as isolating tools. But there are serious obstacles that often stop them isolating in just the right way. These are obstacles that face any model that functions as a thought-experiment but they are especially pressing for economic models because of the paucity of economic principles. Because of the paucity of basic principles, economic models are rich in structural assumptions. Without these no interesting conclusions can (...) be drawn. This, however, makes trouble when it comes to exporting conclusions from the model to the world. One uncontroversial constraint on induction from special cases is to beware of extending conclusions to situations that we know are different in relevant respects. In the case of economic models it is clear by inspection that the unrealistic structural assumptions of the model are intensely relevant to the conclusion. Any inductive leap to a real situation seems a bad bet. (shrink)

This article agrees with Philip Kitcher that we should aim for a well-ordered science, one that answers the right questions in the right ways. Crucial to this is to address questions of use: Which scientific account is right for which system in which circumstances? This is a difficult question: evidence that may support a scientific claim in one context may not support it in another. Drawing on examples in physics and other sciences, this article argues that work on the warrant (...) of theories in philosophy of science needs to change. Emphasis should move from the warrant of theories in the abstract to questions of evidence for use. (shrink)

ContentsPrefaceAnalytical Table of ContentsKevin D. HOOVER: Quantitative Evaluation of Idealized Models in the New Classical MacroeconomicsJohn PEMBERTON: Why Idealized Models in Economics Have Limited UseAmos FUNKENSTEIN: The Revival of Aristotle’s NatureJames R. GRIESEMER: The Informational Gene and the Substantial Body: On the Generalization of Evolutionary Theory byionNancy J. NERSESSIAN: Abstraction via Generic Modeling in Concept Formation in ScienceMargaret MORRISON: Approximating the Real: The Role of Idealizations in Physical TheoryMartin R. JONES: Idealization and Abstraction: A FrameworkDavid S. NIVISON: Standard TimeJames BOGEN (...) and James WOODWARD: Evading the IRSM. Norton WISE: Realism Is DeadRonald N. GIERE: Is Realism Dead? (shrink)

WE AIM HERE to outline a theory of evidence for use. More specifically we lay foundations for a guide for the use of evidence in predicting policy effectiveness in situ, a more comprehensive guide than current standard offerings, such as the Maryland rules in criminology, the weight of evidence scheme of the International Agency for Research on Cancer (IARC), or the US ‘What Works Clearinghouse’. The guide itself is meant to be well-grounded but at the same time to give practicable (...) advice, that is, advice that can be used by policy-makers not expert in the natural and social sciences, assuming they are well-intentioned and have a reasonable but limited amount of time and resources available for searching out evidence and deliberating. (shrink)

We argue against the common view that it is impossible to give a causal account of the distant correlations that are revealed in EPR-type experiments. We take a realistic attitude about quantum mechanics which implies a willingness to modify our familiar concepts according to its teachings. We object to the argument that the violation of factorizability in EPR rules out causal accounts, since such an argument is at best based on the desire to retain a classical description of nature that (...) consists of processes that are continuous in space and time. We also do not think special relativity prohibits the superluminal propagation of causes in EPR, for the phenomenon of quantum measurement may very well fall outside the domain of application of special relativity. It is possible to give causal accounts of EPR as long as we are willing to take quantum mechanics seriously, and we offer two such accounts. (shrink)

Causation is in trouble?at least as it is pictured in current theories in philosophy and in economics as well, where causation is also once again in fashion. In both disciplines the accounts of causality on offer are either modelled too closely on one or another favoured method for hunting causes or on assumptions about the uses to which causal knowledge can be put?generally for predicting the results of our efforts to change the world. The first kind of account supplies no (...) reason to think that causal knowledge, as it is pictured, is of any use; the second supplies no reason to think our best methods will be reliable for establishing causal knowledge. So, if these accounts are all there is to be had, how do we get from method to use? Of what use is knowledge of causal laws that we work so hard to obtain? (shrink)

It is often supposed that the spectacular successes of our modern mathematical sciences support a lofty vision of a world completely ordered by one single elegant theory. In this book Nancy Cartwright argues to the contrary. When we draw our image of the world from the way modern science works - as empiricism teaches us we should - we end up with a world where some features are precisely ordered, others are given to rough regularity and still others behave in (...) their own diverse ways. This patchwork makes sense when we realise that laws are very special productions of nature, requiring very special arrangements for their generation. Combining classic and newly written essays on physics and economics, The Dappled World carries important philosophical consequences and offers serious lessons for both the natural and the social sciences. (shrink)

Reichenbach worked in an era when philosophers were hopeful about the unity of science, and particularly about unity of method. He looked for universal tests of causal connectedness that could be applied across disciplines and independently of specific modeling assumptions. The hunt for quantum causes reminds us that his hopes were too optimistic. The mark method is not even a starter in testing for causal links between outcomes in E.P.R., because our background hypotheses about these links are too thin to (...) supply the kind of information we need to put the method into play. When we turn to conventional statistical methods, we have seen that one test proposed — robustness of the conditional probabilities — can be conclusive only when we know that there are no other causal factors at work. In the particular case of E.P.R., it has often been assumed that this antecedent question can be settled by applying Reichenbach's conjunctive fork condition. But that application is in no way free of further modeling assumptions. Cartwright (1989) has shown that the conjunctive fork is only a necessary condition on a common cause under very limiting restrictions (restrictions that take one far from the case of maximal commonality); and she has argued that these special conditions are not satisfied in E.P.R.Finally, even given the assumption that there are no other causes at work, the significance of robustness for E.P.R. is unclear. We need a model which tells us how one outcome would influence the other; without that, there is no way of interpreting the results of the robustness test so that it is decisive. In each of the approaches we discussed, Reichenbach provided the crucial guiding ideas that underlay our construction of a causality test; but the articulation of a specific criterion depends on the other details of the model. What is a criterion for a specific kind of link in one model need not be in another. We have illustrated with robustness and E.P.R., but we take the point to be perfectly general: there are no tests of causality outside of models which already have significant causal structure built in. (shrink)

Work by social constructionists over the past decade and a half has reenforced the epistemological pessimist's despair that our system of science could ever be a mirror of nature. Realists argue that the amazing success of modern science at precise prediction and control indicates just the contrary. In response, social constructionists often point out that these successes seldom apply to the world as it comes naturally, but only as it is reconstructed in the scientist's laboratory. But this does not explain (...) how scientific accounts, which are as wide of the mark as constructionists suppose in their general descriptions of the natural world, could be so effective in a limited environment, even if that environment is as benignly constructed as possible. This paper suggests that a more wholistic view of nature than modern science usually presupposes may provide an answer. (shrink)

Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal inference, is the ‘flip side’ of an important metaphysical fact about causation—that causes can be used to manipulate their effects. This paper disagrees. First, the premise of their proof does not demand that causes can be used to manipulate their effects but rather that if a relation passes a certain specific kind of test, it is causal. Second, the proof is (...) invalid. Third, the kind of testability they require can easily be had without the causal Markov condition. Introduction Earlier views: manipulability v testability Increasingly weaker theses The proof is invalid MOD* is implausible Two alternative claims and their defects A true claim and a valid argument Indeterminism Overall conclusion. (shrink)

The use of joint distribution functions for noncommuting observables in quantum thermodynamics is investigated in the light of L. Cohen's proof that such distributions are not determined by the quantum state. Cohen's proof is irrelevant to uses of the functions that do not depend on interpreting them as distributions. An example of this, from quantum Onsager theory, is discussed. Other uses presuppose that correlations betweenp andq values depend at least on the state. But correlations may be fixed by the state (...) even though the distribution varies from one ensemble to another represented by that state. Taking covariance as a measure of correlation, it is shown that the different commonly used joint distributions yield the same correlations for a given state. A general characterization is given for a family of distributions with this same covariance. (shrink)

Knowledge of causal laws is expensive and hard to come by. But we work hard to get it because we believe that it will reduce contingency in planning policies and in building new technologies: knowledge of causal laws allows us to predict reliably what the outcomes will be when we manipulate the factors cited as causes in those laws. Or do they? This paper will argue that causal laws have no special role here. As economists from JS Mill to Robert (...) Lucas and David Hendry stress, along recently with philosophers like James Woodward and Sandra Mitchell, they can do the job only if they are invariant under the manipulations proposed. But then, I shall argue, anything that is invariant under the proposed manipulations will do this job equally well. There seems to be nothing special about causal-law knowledge in and of itself that makes it particularly valuable for policy and technology prediction. What seems to matter is invariance alone, not causality. But what guarantees invariance and how do we know when it will obtain? Here certain kinds of causal laws do have a special place – those underwritten either by what I have called ‘nomological machines’ or by what I have called ‘capacities’. Capacities and nomological machines have a double virtue that makes them invaluable for policy planning. First, the causal laws they give rise to will be invariant so long as they obtain; and second, they typically have visible markers we can come to recognize that tell us when they obtain. The markers for nomological machines are shakier than those for capacities, though, since capacities are often tied to markers by well-established empirical laws. Capacities have their own drawback however, which is the final topic of this paper: the causal laws that are guaranteed by a capacity connect the obtaining of a capacity with its exercise. But Hume argued that no distinction can be made between the obtaining of a power and its exercise. (shrink)

In this paper the claim that laws of nature are to be understood as claims about what necessarily or reliably happens is disputed. Laws can characterize what happens in a reliable way, but they do not do this easily. We do not have laws for everything occurring in the world, but only for those situations where what happens in nature is represented by a model: models are blueprints for nomological machines, which in turn give rise to laws. An example from (...) economics shows, in particular, how we use--and how we need to use--models to get probabilistic laws. (shrink)

In much recent work, invariance under intervention has become a hallmark of the correctness of a causal-law claim. Despite its importance this thesis generally is either simply assumed or is supported by very general arguments with heavy reliance on examples, and crucial notions involved are characterized only loosely. Yet for both philosophical analysis and practicing science, it is important to get clear about whether invariance under intervention is or is not necessary or sufficient for which kinds of causal claims. Furthermore, (...) we need to know what counts as an intervention and what invariance is. In this paper I offer explicit definitions of two different kinds for the notions intervention, invariance, and causal correctness. Then, given some natural and relatively uncontroversial assumptions, I prove two distinct sets of theorems showing that invariance is indeed a mark of causality when the concepts are appropriately interpreted. (shrink)

Empirical adequacy matters directly - as it does for antirealists - if we aim to get all or most of the observable facts right, or indirectly - as it does for realists - as a symptom that the claims we make about the theoretical facts are right. But why should getting the facts - either theoretical or empirical - right be required of an acceptable theory? Here we endorse two other jobs that good theories are expected to do: helping us (...) with a) understanding and b) managing the world. Both are of equal, often greater, importance than getting a swathe of facts right, and empirical adequacy fares badly in both. It is not needed for doing these jobs and in many cases it gets in the way of doing them efficiently. (shrink)