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.
Rethinking Order Cartwright, Nancy; Ward, Keith
2016, 2016-06-30
eBook
This book presents a radical new picture of natural order. The Newtonian idea of a cosmos ruled by universal and exceptionless laws has been superseded; replaced by a conception of nature as a realm ...of diverse powers, potencies, and dispositions, a ‘dappled world’. There is order in nature, but it is more local, diverse, piecemeal, open, and emergent than Newton imagined. In each chapter expert authors expound the historical context of the idea of laws of nature, and explore the diverse sorts of order actually presupposed by work in physics, biology, and the social sciences. They consider how human freedom might be understood, and explore how Newton’s idea of a ‘universal designer’ might be revised, in this new context.
Randomized Controlled Trials (RCTs) are increasingly popular in the social sciences, not only in medicine. We argue that the lay public, and sometimes researchers, put too much trust in RCTs over ...other methods of investigation. Contrary to frequent claims in the applied literature, randomization does not equalize everything other than the treatment in the treatment and control groups, it does not automatically deliver a precise estimate of the average treatment effect (ATE), and it does not relieve us of the need to think about (observed or unobserved) covariates. Finding out whether an estimate was generated by chance is more difficult than commonly believed. At best, an RCT yields an unbiased estimate, but this property is of limited practical value. Even then, estimates apply only to the sample selected for the trial, often no more than a convenience sample, and justification is required to extend the results to other groups, including any population to which the trial sample belongs, or to any individual, including an individual in the trial. Demanding ‘external validity’ is unhelpful because it expects too much of an RCT while undervaluing its potential contribution. RCTs do indeed require minimal assumptions and can operate with little prior knowledge. This is an advantage when persuading distrustful audiences, but it is a disadvantage for cumulative scientific progress, where prior knowledge should be built upon, not discarded. RCTs can play a role in building scientific knowledge and useful predictions but they can only do so as part of a cumulative program, combining with other methods, including conceptual and theoretical development, to discover not ‘what works’, but ‘why things work’.
•Randomization does not balance confounders in any single trial.•Unbiasedness is of limited practical value compared with precision.•Asymmetric distributions of treatment effects pose threats to significance testing.•The best method depends on hypothesis tested, what's known, and cost of mistakes.•RCT results can serve science but are weak ground for inferring ‘what works’.
For evidence-based practice and policy, randomised controlled trials (RCTs) are the current gold standard. The metaphysical assumption aside, support-though no guarantee-for premises 2 and 3 is built ...right into RCT design: premise 2, by policing of treatment administration, blinding, random assignment, and the like; premise 3, by techniques-including large sample size-for reliably inferring probabilities from observed frequencies. ... although knowledge that a treatment reliably promotes an outcome is evidence that it will cause that outcome for us, it is only part of an evidential argument.
This paper defends the need for evidential diversity and the mix of methods that that can in train require. The focus is on causal claims, especially ‘singular’ claims about the effects of causes in ...a specific setting—either what will happen or what has happened. I do so by offering a template that categorises kinds of evidence that can support these claims. The catalogue is generated by considering what needs to happen for a causal process to carry through from putative cause at the start to the targeted effect at the end. The usual call for mixed methods focusses on a single overall claim and argues that we increase certainty by the use of different methods with compensating strengths and weaknesses. My proposals instead focus on the evidence that supports the great many subsidiary claims that must hold if the overall one is to be true. As is typical for singular causal claims, the mix of methods that will generally be required to collect the kinds of evidence I urge will usually have little claim to the kind of rigour that is now widely demanded in evidencing causal claims, especially those for policy/treatment effectiveness. So I begin with an exploration of what seems to be intended by ‘rigour’ in such discussions, since it is seldom made clear just what makes the favoured methods especially rigorous. I then argue that the emphasis on rigour can be counterproductive. Rigour is often the enemy of evidential diversity, and evidential diversity—lots of it—can make for big improvements in the reliability of singular causal predictions and post hoc evaluations. I illustrate with the paragon of rigour for causal claims, randomised controlled trials (RCTs), rehearsing at some length what they can and cannot do to make it easier to assess the importance of rigour in warranting singular causal claims.
Middle-range theory Cartwright, Nancy
Theoria (Madrid, Spain),
01/2020, Letnik:
35, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Philosophers of science have had little to say about 'middle-range theory' although much of what is done in science and of what drives its successes falls under that label. These lectures aim to ...spark an interest in the topic and to lay groundwork for further research on it. 'Middle' in 'middle range' is with respect to the level both of abstraction and generality. Much middle-range theory is about things that come under the label 'mechanism'. The lectures explore three different kinds of mechanism: structural mechanisms or underlying systems that afford causal pathways; causal-chain mechanisms that are represented in what in policy contexts are called 'theories of change' and for which I give an extended account following the causal process theory of Wesley Salmon; and middle-range-law mechanisms like those discussed by Jon Elster, which I claim are —and rightly are— rampant throughout the social sciences. The theory of the democratic peace, that democracies do not go to war with democracies, serves as a running example. The discussions build up to the start of, first, an argument that reliability in social (and natural) science depends not so much on evidence as it does on the support of a virtuous tangle of practices (without which there couldn't even be evidence), and second, a defence of a community-practice centred instrumentalist understanding of many of the central basic principles that we use (often successfully) in social (and in natural) science for explanation, prediction and evaluation.
Los filósofos de la ciencia han tenido poco que decir acerca de la "teoría de rango medio", aunque gran parte de lo que se hace en la ciencia y de lo que impulsa sus éxitos cae bajo esa etiqueta. Estas conferencias tienen como objetivo despertar el interés en el tema y sentar las bases para la ulterior investigación al respecto. "Medio" en "rango medio" hace referencia al nivel de abstracción y generalidad. Gran parte de la teoría de rango medio trata sobre cosas que caen bajo la etiqueta de "mecanismo". Las conferencias exploran tres tipos diferentes de mecanismos: mecanismos estructurales o sistemas subyacentes que permiten vías causales; mecanismos de cadena causal que están representados en lo que en contextos de política se denominan "teorías de cambio" y sobre los cuales doy una explicación extensa siguiendo la teoría del proceso causal de Wesley Salmon; y mecanismos de ley de rango medio como los discutidos por Jon Elster, que afirmo son, y con razón son, rampantes en todas las ciencias sociales. La teoría de la paz democrática, que las democracias no van a la guerra con las democracias, sirve como un ejemplo en funcionamiento. Las discusiones se desarrollan hasta el comienzo de, primero, un argumento según el que la confiabilidad en las ciencias sociales (y naturales) no depende tanto de la evidencia como del apoyo de una maraña virtuosa de prácticas (sin las cuales ni siquiera podría haber evidencia), y en segundo lugar, una defensa de una comprensión instrumentalista, centrada en la práctica comunitaria, de muchos de los principios básicos centrales que usamos (a menudo con éxito) en ciencias sociales (y naturales) para explicar, predecir y evaluar.
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.
There is a takeover movement fast gaining influence in development economics, a movement that demands that predictions about development outcomes be based on randomized controlled trials. The problem ...it takes up—of using evidence of efficacy from good studies to predict whether a policy will be effective if we implement it—is a general one, and affects us all. My discussion is the result of a long struggle to develop the right concepts to deal with the problem of warranting effectiveness predictions. Whether I have it right or not, these are questions of vast social importance that philosophers of science can, and should, help answer.
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