The use of multiple means of determination to “triangulate” on the existence and character of a common phenomenon, object, or result has had a long tradition in science but has seldom been a matter of primary focus. As with many traditions, it is traceable to Aristotle, who valued having multiple explanations of a phenomenon, and it may also be involved in his distinction between special objects of sense and common sensibles. It is implicit though not emphasized in the distinction between (...) primary and secondary qualities from Galileo onward. It is arguably one of several conceptions involved in Whewell’s method of the “consilience of inductions” (Laudan 1971) and is to be found in several places in Peirce. (From M. Brewer and B. Collins, eds., (1981); Scientific Inquiry in the Social Sciences (a festschrift for Donald T. Campbell), San Francisco: Jossey-Bass, pp. 123–162.). (shrink)

Causal pluralists hold that that there is not just one determinate kind of causation. Some causal pluralists hold that ‘cause’ is ambiguous among these different kinds. For example, Hall (2004) argues that ‘cause’ is ambiguous between two causal relations, which he labels dependence and production. The view that ‘cause’ is ambiguous, however, wrongly predicts zeugmatic conjunction reduction, and wrongly predicts the behaviour of ellipsis in causal discourse. So ‘cause’ is not ambiguous. If we are to disentangle causal pluralism from the (...) ambiguity claim, we need to consider what other linguistic approaches are available to the causal pluralist. I consider and reject proposals that ‘cause’ is a general term, that the term is an indexical, and that the term conveys different kinds of causation through implicature or presupposition. Finally, I argue that causal pluralism is better handled by treating ‘cause’ as a univocal term within a dynamic interpretation framework. (shrink)

Unentwegt werden neue technische Produkte gestaltet. Doch was macht die technische Gestaltung aus? Wie lässt sich ihr Gegenstand - (noch) nicht existierende Artefakte - adäquat auf den Begriff bringen? Michael Kuhn begreift technische Ideen vor ihrer Realisierung als Fiktionen. Er bietet eine fiktionstheoretische Rekonstruktion der Gestaltungstätigkeit und entwickelt hieraus eine Ethik der Gestaltung. Der stark interdisziplinäre Zugang zwischen Technikphilosophie und Ingenieurwissenschaften liefert neue Erkenntnisse für beide Fachrichtungen und stellt wertvolle Grundlagen bereit.

Manipulationism holds that information about the results of interventions is of utmost importance for scientific practices such as causal assessment or explanation. Specifically, manipulation provides information about the stability, or invariance, of the relationship between X and Y: were we to wiggle the cause X, the effect Y would accordingly wiggle and, additionally, the relation between the two will not be disrupted. This sort of relationship between variables are called 'invariant empirical generalisations'. The paper focuses on questions about causal assessment (...) and analyses the status of manipulation. It is argued that manipulationism is trapped in a dilemma. If manipulationism is read as providing a conceptual analysis of causation, then it fails to provide a story about the methods for causal assessment. If, instead, manipulationism is read as providing a method for causal assessment, then it is at an impasse concerning causal assessment in areas where manipulations are not performed. Empirical generalisations are then reassessed, in such a way that manipulation is not taken as methodologically fundamental. The paper concludes that manipulation is the appropriate tool for some scientific contexts, but not for all. (shrink)

Science as we know it is “dappled”. Its picture of the world is a mosaic in which different aspects of the world, different systems, are represented by narrow-scope theories or models that are largely disconnected from one another. The best explanation for this disunity in our representation of the world, Nancy Cartwright has proposed, is a disunity in the world itself: rather than being governed by a small set of strict fundamental laws, events unfold according to a patchwork of principles (...) covering different kinds of systems or segments of reality, each with something less than full omnipotence and with the possibility of anomic indeterminism at the boundaries. This paper attempts to undercut Cartwright’s argument for a dappled world by showing that the motley nature of science, both now and even at the completion of empirical inquiry, can equally well be explained by proponents of the “fundamentalist” view that the universe’s initial conditions and fundamental physical laws determine everything that ever happens. (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)

Among the many causes of an event, how do we distinguish the important ones? Are there ways to distinguish among causes on principled grounds that integrate both practical aims and objective knowledge? Psychologist Tania Lombrozo has suggested that causal explanations “identify factors that are ‘exportable’ in the sense that they are likely to subserve future prediction and intervention” (Lombrozo 2010, 327). Hence portable causes are more important precisely because they provide objective information to prediction and intervention as practical aims. However, (...) I argue that this is only part of the epistemology of causal selection. Recent work on portable causes has implicitly assumed them to be portable within the same causal system at a later time. As a result, it has appeared that the objective content of causal selection includes only facts about the causal structure of that single system. In contrast, I present a case study from systems biology in which scientists are searching for causal factors that are portable across rather than within causal systems. By paying careful attention to how these biologists find portable causes, I show that the objective content of causal selection can extend beyond the immediate systems of interest. In particular, knowledge of the evolutionary history of gene networks is necessary for correctly identifying causal patterns in these networks that explain cellular behavior in a portable way. (shrink)

This volume is a collation of original contributions from the key actors of a new trend in the contemporary theory of knowledge and belief, that we call “dynamic epistemology”. It brings the works of these researchers under a single umbrella by highlighting the coherence of their current themes, and by establishing connections between topics that, up until now, have been investigated independently. It also illustrates how the new analytical toolbox unveils questions about the theory of knowledge, belief, preference, action, and (...) rationality, in a number of central axes in dynamic epistemology: temporal, social, probabilistic and even deontic dynamics. (shrink)

Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion (...) by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. (shrink)

This paper evaluates Nancy Cartwright’s critique of economic models. Cartwright argues that economics fails to build relevant “nomological machines” able to isolate capacities. In this paper, I contend that many economic models are not used as nomological machines. I give some evidence for this claim and build on an inferential and pragmatic approach to economic modeling. Modeling in economics responds to peculiar inferential norms where a “good” model is essentially a model that enhances our knowledge about possible worlds. As a (...) consequence, models and experiments are very different knowledge-producing devices, at least in economics. (shrink)

This editorial offers new ways to ethically practice, evaluate, and use quantitative research. Our central claim is that ready-made formulas for QR, including ‘best practices’ and common notions of ‘validity’ or ‘objectivity,’ are often divorced from the ethical and practical implications of doing, evaluating, and using QR for specific purposes. To focus on these implications, we critique common theoretical foundations for QR and then recommend approaches to QR that are ‘built for purpose,’ by which we mean designed to ethically address (...) specific problems or situations on terms that are contextually relevant. For this, we propose a new tool for evaluating the quality of QR, which we call ‘relational validity.’ Studies, including their methods and results, are relationally valid when they ethically connect researchers’ purposes with the way that QR is oriented and the ways that it is done—including the concepts and units of analysis invoked, as well as what its ‘methods’ imply more generally. This new way of doing QR can provide the liberty required to address serious worldly problems on terms that are both practical and ethically informed in relation to the problems themselves rather than the confines of existing QR logics and practices. (shrink)

The interventionist theory of causation has been advertised as an empirically informed and more nuanced approach to causality than the competing theories. However, previous literature has not yet analyzed the regression discontinuity (hereafter, RD) and the difference-in-differences (hereafter, DD) within an interventionist framework. In this paper, I point out several drawbacks of using the interventionist methodology for justifying the DD and RD designs. However, I argue that the first step towards enhancing our understanding of the DD and RD designs from (...) an interventionist perspective is to take advantage of the assumptions of common trend and continuity. (shrink)

Explanations are backed by many different relations: causation, grounding, and arguably others too. But why are these different relations capable of backing explanations? In virtue of what are they explanatory? In this paper, I propose and defend a monistic account of explanation-backing relations. On my account, there is a single relation which backs all cases of explanation, and which explains why those other relations are explanation-backing.

It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions (...) are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities. (shrink)

In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere's theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, (...) and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere's theory in other respects. (shrink)

(No abstract is available for this citation).

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...) patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

Nancy Cartwright begins her recent book, Hunting Causes and Using Them, by noting that while a few years ago real causal claims were in dispute, nowadays “causality is back, and with a vengeance.” In the case of the social sciences, Keith Morrison writes that “Social science asks ‘why?’. Detecting causality or its corollary—prediction—is the jewel in the crown of social science research.” With respect to the health sciences, Judea Pearl writes that the “research questions that motivate most studies in the (...) health sciences are causal in nature.” However, not all data used by people interested in making causal claims come from experiments that use random assignment to control and treatment groups. Indeed, much research in the social and health science depends on non-experimental, observational data. Thus, one of the most important problems in the social and health sciences concerns making warranted causal claims using non-experimental, observational data; viz., “Can observational data be used to make etiological inferences leading to warranted causal claims?” This paper examines one method of warranting causal claims that is especially widespread in epidemiology and the health sciences generally—the use of causal criteria. It is argued that cases of complex causation generally, and redundant causation—both causal overdetermination and causal preemption—specifically, undermine the use of such criteria to warrant causal claims. (shrink)

Realizing the ideal of democracy requires political inclusion for citizens. A legitimate democracy must give citizens the opportunity to express their attitudes about the relative attractions of different policies, and access to political mechanisms through which they can be counted and heard. Actual governance often aims not at accurate belief, but at nonepistemic factors like achieving and maintaining institutional stability, creating the feeling of government legitimacy among citizens, or managing access to influence on policy decision-making. I examine the traditional relationship (...) between inclusiveness and accuracy, and illustrate this connection by discussing empirical work on how group decision-making can improve accuracy. I also advance a Generic Epistemic Principle that any evidence-based decision-making procedures must embrace. Focusing on policy-making, I then measure the distance between these standards and the ones actually implemented in U.S. political settings. Psychological research on individual and group decision-making is a source of normative assessment for existing policy judgment, but it neither rationalizes nor legitimates the actual and typical processes used in U.S. institutions of political decision making. To establish this point, I focus on one characteristic government institution—the U.S. House of Representatives Committee on Science, Space, and Technology—that displays deliberative processes at odds with the sciences they advocate, and with the Generic Epistemic Principle. I explain this discouraging condition in terms of several inveterate factors in U.S. politics: a limitlessly money-driven and endless campaigning process that effectively forces elected representatives to align themselves with money and vote strategically, the use of procedural arrangements known to make people feel politically included when they are not, and the unresponsiveness of a majoritarian (vs. consensus) democracy. (shrink)

This paper offers a practical argument for metaphysical emergence. The main message is that the growing reliance on so-called irrational scientific methods provides evidence that objects of science are indecomposable and as such, are better described by metaphysical emergence as opposed to the prevalent reductionistic metaphysics. I show that a potential counterargument that science will eventually reduce everything to physics has little weight given where science is heading with its current methodological trend. I substantiate my arguments by detailed examples from (...) biological engineering, but the conclusions are extendable beyond that discipline. (shrink)

We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather than an (...) add-on methodological assumption of simplicity, necessarily follows from the substantive interventionist theses, provided that the actual probability distribution is strictly positive. Second, we demonstrate that the causal minimality condition can fail when the actual probability distribution is not positive, as is the case in the presence of deterministic relationships. But we argue that the interventionist account still entails a pragmatic justification of the causal minimality condition. Our argument in the second part exemplifies a general perspective that we think commendable: when evaluating methods for inferring causal structures and their underlying assumptions, it is relevant to consider how the inferred causal structure will be subsequently used for counterfactual reasoning. (shrink)

Why do classic biostatistical studies, alleged to provide causal explanations of effects, often fail? This article argues that in statistics-relevant areas of biology—such as epidemiology, population biology, toxicology, and vector ecology—scientists often misunderstand epistemic constraints on use of the statistical-significance rule (SSR). As a result, biologists often make faulty causal inferences. The paper (1) provides several examples of faulty causal inferences that rely on tests of statistical significance; (2) uncovers the flawed theoretical assumptions, especially those related to randomization, that likely (...) contribute to flawed biostatistics; (3) re-assesses the three classic (SSR-warrant, avoiding-selection-bias, and avoiding-confounders) arguments for using SSR only with randomization; and (4) offers five new reasons for biologists to use SSR only with randomized experiments. (shrink)

This article suggests a revision of the theory of causal nets. In section 1 we introduce an axiomatization of TCN based on a realistic understanding. It is shown that the causal Markov condition entails three independent principles. In section 2 we analyze indeterministic decay as the major counterexample to one of these principles: screening off by common causes. We call SCC-violating common causes interactive causes. In section 3 we develop a revised version of TCN, called TCN*, which accounts for interactive (...) causes. It is shown that there are interactive causal models that admit of no faithful noninteractive reconstruction. (shrink)

Grounding is often glossed as metaphysical causation, yet no current theory of grounding looks remotely like a plausible treatment of causation. I propose to take the analogy between grounding and causation seriously, by providing an account of grounding in the image of causation, on the template of structural equation models for causation.

Two key ideas of scientific explanation−explanation as causal information and explanation as unification-have frequently been set into mutual opposition. This paper proposes a “dialectical solution” to this conflict, by arguing that causal explanations are preferable to non-causal ones, because they lead to a higherdegree of unification at the level of explaining statistical regularities. The core axioms of the theory of causal nets (TC) are justified because they offer the best if not the only unifying explanation of two statistical phenomena: screening (...) off and linking up. Alternative explanations of the two phenomena are discussed and it isshown why they don’t work. It is demonstrated that although the core axioms of TC are empirically vacuous, extended versions of TC have empirical content by means of which they can generate independently testable predictions.Con frecuencia se han planteado como contrapuestas dos ideas clave en la explicacion cientifica (explicacion como informacion causal y explicacion como unificacion). El presente articulo propone una “solucion dialectica” argumentando que las explicaciones causales son preferibles a las no-causales porque aquellas comportan un mayor grado de unificacion en la explicacion de regularidades estadisticas. Los axiomas centrales de la teoria de redes causales (TC) estan justificados porque ofrecen la mejor, si no la unica, explicacion unificada de dos fenomenos estadisticos: neutralizacion (screening off) y vinculacion (linking up). Se discuten las explicaciones alternativas de estos dos fenomenos y se razona por que no funcionan. Se demuestra ademas que aunque los axiomas centrales de TC son empiricamente vacuos, las versiones extendidas de TC tienen un contenido empirico gracias al cual pueden generar predicciones independientemente contrastables. (shrink)

We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be understood as a theoretical concept that is not explicated by a single definition, but by the axioms of a theory. Such an understanding of causality implicitly underlies the well-known theory of causal nets and has been explicitly promoted by Glymour. In this paper we investigate the explanatory warrant and empirical content of TCN. We sketch how the assumption of directed cause–effect relations can be (...) philosophically justified by an inference to the best explanation. We then ask whether the explanations provided by TCN are merely post-facto or have independently testable empirical content. To answer this question we develop a fine-grained axiomatization of TCN, including a distinction of different kinds of faithfulness. A number of theorems show that although the core axioms of TCN are empirically empty, extended versions of TCN have successively increasing empirical content. (shrink)

Philosophers have debated at length the epistemological status of scientific thought experiments. I contend that the literature on this topic still lacks a common conceptual framework, a lacuna that produces radical disagreement among the participants in this debate. To remedy this problem, I suggest focusing on the distinction between the internal and the external validity of an experiment, which is also crucial for thought experiments. I then develop an account of both kinds of validity in the context of thought experiments. (...) I show that we can naturally conceptualise internal validity in terms of games of make-believe. Then, I argue that external validity is best defined as accurate representation of a target system. Finally, I turn back to the current debate on thought experiments and show that my diarchic account provides a general framework that can be shared by the competing philosophical views, as well as a fruitful guide for their reconciliation. (shrink)

Our research addresses the issue of teaching and learning concepts in science education as an empirical question. We study the process of conceptualization by closely examining the unfolding of classroom lesson sequences. We situate our work within the practice turn line of research on epistemic practices in science education. We also adopt a practice turn approach when it comes to the learning of concepts, as we consider conceptualization as being inherent within epistemic practices. In our work, pedagogical practices are modeled (...) as learning games and epistemic practices in science education are characterized as enacted epistemic games emerging through the unfolding of learning games. Science practices are modeled as source epistemic games since they are the source of the knowledge at stake in pedagogical practices. From this point, we examine closely how playing learning games can enable students to play enacted epistemic games and then in turn the source epistemic games at the core of conceptual understanding. Thus, the main contribution of this paper is to link pedagogical practices to epistemic practices in science education and to science practices in general. Our method is consistent with this epistemological framework as our case study on the concept of earthquakes in a 5th grade classroom sequence illustrates. Following an investigation of two experienced teachers and their classes during a teaching unit, our analysis shows how teaching effectiveness is determined by a dialectic. This entails on the one hand a didactic continuity between learning games and enacted epistemic games and, on the other, an epistemic continuity between enacted epistemic games and source epistemic games. (shrink)

This paper aims to provide an answer to James Woodward’s article “Flagpoles anyone? Causal and explanatory asymmetries”. It will be conjectured that, when causal directionality depends on the experimental design, it is because the variables involved are capable of producing changes in each other. This will be exemplified using the case of ideal gases as opposed to the flagpole-shadow scenario.

Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to accredited positions in the philosophy of causality and in social science methodology, invariance under intervention provides the most reliable test to decide whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal (...) methodology; consequently, the argument goes, the account is ill-suited to those contexts where manipulations are not performed, for instance, the social sciences. The article aims to extend the account of invariance (under intervention), in a way that manipulations on the putative causal factors are not methodologically fundamental, and yet invariance remains key for causal assessment both in experimental and non-experimental contexts. (shrink)

The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the fact (...) that causes be defined in terms of ‘difference-making’, this cannot fixes the causal metaphysics. Causality in epidemiology ought to be interpreted according to the epistemic theory. In this approach relations are deemed causal depending on the evidence and on the available methods. Indeed, evidence to establish causal claims requires difference-making considerations; furthermore, those definitions of cause reflect the ‘variational’ epistemology and methodology of epidemiology. (shrink)

A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This paper, narrowing down the scope to quantitative causal analysis in social science, reformulates the problem in terms of the validity of statistical models. Two strategies to make sense of correlational data are presented: first, a 'structural strategy', the goal of which (...) is to model and test causal structures that explain correlational data; second, a 'manipulationist or interventionist strategy', that hinges upon the notion of invariance under intervention. It is argued that while the former can offer a solution the latter cannot. (shrink)

In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only 'closed' and 'hermetic' systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be compatible and whether causal analysis can be integrated into the (...) broader framework of system analysis. This article attempts a negative answer on the grounds of fundamental differences in their assumptions and suggests using system analysis as a post-hoc comparative tool. (shrink)

Evidential pluralism has been used to justify mixed-method research in political science. The combination of methodologies within case study analysis, however, has not received as much attention. This article applies the theory of evidential pluralism to causal inference in the case study method process tracing. I argue that different methodologies for process tracing commit to distinct fundamental theories of causation. I show that, problematically, one methodology may not recognize as genuine knowledge the fundamental claims of the other. By evaluating the (...) epistemic reliability of these fundamental claims, we can find a way out of such conflicts and rescue pluralism. (shrink)

Naturalism in the philosophy of science has proceeded differently than the familiar forms of meta-philosophical naturalism in other sub-fields, taking its cues from “science as we know it” (Cartwright in The Dappled World, Oxford University Press, Oxford, 1999, p. 1) rather than from a philosophical conception of “the Scientific Image.” Its primary focus is scientific practice, and its philosophical analyses are complementary and accountable to empirical studies of scientific work. I argue that naturalistic philosophy of science is nevertheless criterial for (...) other versions of meta-philosophical naturalism; relying on a conflicting conception of scientific understanding would constitute a “first philosophy” imposed on the sciences. Moreover, naturalistic philosophy of science provides the basis for a “radically” naturalistic alternative to the familiar forms of orthodox or liberal naturalism. Goodman, Sellars and Hempel had previously challenged empiricist scruples against causal connections or nomological necessity by arguing that scientific concepts already had modal import. The radical naturalism I defend similarly challenges meta-philosophical naturalists’ conception of the Scientific Image as anormative, and instead shows how the normativity of scientific understanding in practice is a scientifically intelligible natural phenomenon. This account then provides a basis for naturalistic reflection on how other practices and normative concerns fit together with the best scientific understanding of human ways of life. (shrink)

To what use can causal claims established in good policy studies be put? We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it to carry (...) over to the target. Abstraction is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge.¿Qué uso podemos hacer de las tesis causales que encontramos en los buenos estudios sobre política aplicada? Distinguimos dos razones por las que pueden fallar las inferencias desde la población en el estudio a la población general. En primer lugar, las variables que usamos en política no generan resultados por sí solas. Necesitan factores coadyuvantes. La distribución de estos factores es probablemente única o local en cada estudio, así que no hay motivos para esperar su validez externa. En segundo lugar, los investigadores a menudo dan descripciones demasiado concretas de la causalidad en el estudio como para poder generalizarlas. La abstracción es necesaria para obtener causas que viajen. No hay ningún modo absolutamente seguro de evitar semejantes fallos. Pero esto no implica que no haya arreglos subóptimos. De nuestro diagnóstico se siguen dos orientaciones generales sobre las buenas prácticas en la asesoría política: concentrémonos en los detalles concretos de la población en la que pretendemos intervenir y usemos heurísticas transdisciplinares que diversifiquen nuestro conocimiento de fondo. (shrink)

To what use can causal claims established in good studies be put? We give examples of studies from which inaccurate inferences were made about target policy situations. The usual diagnosis is that the studies in question lack external validity, which means that the same results do not hold in the target as in study. That’s a label that just repeats what we already knew. We offer a deeper analysis. Our analysis points to the need for interdisciplinarity and to the demand (...) to focus not on the study – as the expression ‘external validity’ invites you to do – but on the target. The call for interdisciplinary approaches to real life problems is common since it is widely acknowledged that what happens in the real world seldom falls under the auspices of any single research domain. Our focus is on one specific real life problem: how to use causal claims from good studies to help predict whether the policies tested will work in a new situation. Our analysis of what it takes to back up these predictions points up very specific stages in the process of prediction where we are bound to get it wrong if we do not diversify our concepts, our knowledge and our methods. We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it to carry over to the target.ion is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge. (shrink)

ABSTRACTMeasurements of environmental toxicity from long-term exposure to oil contamination have delivered inaccurate and contradictory results regarding the potential harms for humans and ecosyste...

In this paper I look at causality in the context of intervention research, and discuss some problems faced in the evaluation of causal hypotheses via interventions. I draw attention to a simple problem for evaluations that employ randomized controlled trials. The common alternative to randomized trials, the observational study, is shown to face problems of a similar nature. I then argue that these problems become especially acute in cases where the intervention is complex (i.e. that involves intervening in a complex (...) system). Finally, I consider and reject a possible resolution of the problem involving the simulation of complex interventions. The conclusion I draw from this is that we need to radically reframe the way we think about causal inference in complex intervention research. (shrink)

Cartwright (Synthese 121(1/2):3–27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios feature a certain kind of non-causal dependence that kicks in once (...) the common cause occurs. We then develop a representation of this specific kind of non-causal dependence that allows for modeling the problematic scenarios in such a way that the Markov condition is not violated anymore. (shrink)

All univocal analyses of causation face counterexamples. An attractive response to this situation is to become a pluralist about causal relationships. "Causal pluralism" is itself, however, a pluralistic notion. In this article, I argue in favor of pluralism about concepts of cause in the social sciences. The article will show that evidence for, inference from, and the purpose of causal claims are very closely linked. Key Words: causation • pluralism • evidence • methodology.

Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm. Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side (...) effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. In this article, we expand the Bayesian framework and add “evidential modulators,” which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, “E-Synthesis”, is then applied to a case study. Results: Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework. Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is philosophically and statistically grounded. Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses. (shrink)

It is argued that in deterministic contexts evidence for causal relations states whether a boundary condition makes a difference or not to a phenomenon. In order to substantiate the analysis, I show that this difference/indifference making is the basic type of evidence required for eliminative induction in the tradition of Francis Bacon and John Stuart Mill. To this purpose, an account of eliminative induction is proposed with two distinguishing features: it includes a method to establish the causal irrelevance of boundary (...) conditions by means of indifference making, which is called strict method of agreement, and it introduces the notion of a background against which causal statements are evaluated. Causal statements thus become three-place-relations postulating the relevance or irrelevance of a circumstance C to the examined phenomenon P with respect to a background B of further conditions. To underline the importance of evidence in terms of difference/indifference making, I sketch two areas, in which eliminative induction is extensively used in natural and engineering sciences. One concerns exploratory experiments, the other engineering design methods. Given that a method is discussed that has been used for centuries, I make no claims to novelty in this paper, but hope that the combined discussion of several topics that are still somewhat underrepresented in the philosophy of science literature is of some merit. (shrink)

Philosophers of science have often favoured reductive approaches to how-possibly explanation. This article identifies three alternative conceptions making how-possibly explanation an interesting phenomenon in its own right. The first variety approaches “how possibly X?” by showing that X is not epistemically impossible. This can sometimes be achieved by removing misunderstandings concerning the implications of one’s current belief system but involves characteristically a modification of this belief system so that acceptance of X does not result in contradiction. The second variety offers (...) a potential how-explanation of X. It is usually followed by a range of further potential how-explanations of the same phenomenon. In recent literature the factual claims implied by the second variety have been downplayed whereas the heuristic role of mapping the space of conceptual possibilities has been emphasized. I will focus especially on this truth-bracketing sense of potentiality when looking closer at the second variety in the paper. The third variety has attracted less interest. It presents a partial how-explanation of X. Typically it aims to establish the existence of a mechanism by which X could be and was generated. The third conception stands out as the natural alternative for the advocate of ontic how-possibly explanations. This article transfers Salmon’s view that explanation-concepts can be broadly divided into epistemic, modal, and ontic to the context of how-possibly explanations. Moreover, it is argued that each of the three above-mentioned varieties of how-possibly explanation occurs in science. To recognize this may be especially relevant for philosophers. We are often misled by the promises of various why-explanation accounts, and seem to have forgotten nearly everything about the diversity of how-possibly explanations. (shrink)

This paper provides empirical interpretation of the do(x)do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x)do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally (...) eligible to receive the do(x)do(x) operator and to produce useful information for decision makers. (shrink)