The notion of common cause closedness of a classical, Kolmogorovian probability space with respect to a causal independence relation between the random events is defined, and propositions are presented that characterize common cause closedness for specific probability spaces. It is proved in particular that no probability space with a finite number of random events can contain common causes of all the correlations it predicts; however, it is demonstrated that probability spaces even with a finite number of random events can be (...) common cause closed with respect to a causal independence relation that is stronger than logical independence. Furthermore it is shown that infinite, atomless probability spaces are always common cause closed in the strongest possible sense. Open problems concerning common cause closedness are formulated and the results are interpreted from the perspective of Reichenbach's Common Cause Principle. (shrink)

Russell famously argued that causation should be dispensed with. He gave two explicit arguments for this conclusion, both of which can be defused if we loosen the ties between causation and determinism. I show that we can define a concept of causation which meets Russell’s conditions but does not reduce to triviality. Unfortunately, a further serious problem is implicit beneath the details of Russell’s arguments, which I call the causal exclusion problem. Meeting this problem involves deploying a minimalist pragmatic account (...) of the nature and function of modal language. Russell’s scruples about causation can be accommodated, even as we partially legitimise the pervasive causal explanations in folk and scientific practice. (shrink)

Philosophers have used the probabilistic relation of ’screening-off‘ to explicate concepts in the theories of causation and explanation. Brandon has used screening-off relations in an attempt to reconstruct an argument of Mayr and Gould that natural selection acts at the level of the organism. I argue that Brandon‘s reconstruction is unsuccessful.

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...) mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation. (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)

In this paper we defend the view that the ordinary notions of cause and effect have a direct and essential connection with our ability to intervene in the world as agents.1 This is a well known but rather unpopular philosophical approach to causation, often called the manipulability theory. In the interests of brevity and accuracy, we prefer to call it the agency theory.2 Thus the central thesis of an agency account of causation is something like this: an event A is (...) a cause of a distinct event B just in case bringing about the occurrence of A would be an effective means by which a free agent could bring about the occurrence of B. In our view the unpopularity of the agency approach to causation may be traced to two factors. The first is a failure to appreciate certain distinctive advantages that this approach has over its various rivals. We have drawn attention to some of these advantages elsewhere, and we summarize below. However, the second and more important factor is the influence of a number of stock objections, objections that seem to have persuaded many philosophers that agency accounts face insuperable obstacles. In this paper we want to show that these objections have been vastly overrated. There are four main objections. (shrink)

Bayesian networks are computer programs which represent probabilitistic relationships graphically as directed acyclic graphs, and which can use those graphs to reason probabilistically , often at relatively low computational cost. Almost every expert system in the past tried to support probabilistic reasoning, but because of the computational difficulties they took approximating short-cuts, such as those afforded by MYCIN's certainty factors. That all changed with the publication of Judea Pearl's Probabilistic Reasoning in Intelligent Systems, in 1988, which synthesized a decade of (...) research making accurate graphical probabilistic reasoning computationally achievable.Bayesian network technology is now one of the fastest growing fields of research in artificial intelligence. That it has become a publication industry in its own right is shown by a search on Google scholar :This development, together with a parallel related growth in the use of causal discovery algorithms which automate the learning of Bayesian networks from sample data, has generated considerable interest, and controversy, within the philosophy-of-science community.Three central questions bringing together AI researchers and philosophers of science are: Are Bayesian networks Bayesian? What is the relation between probability and causality? Are the assumptions behind causal discovery of Bayesian networks realistic or fantastical?Jon Williamson, as a philosopher of science with a keen interest in the technology, asks and answers these questions in his new book. Although it is self-contained, his book is not very likely as an introduction to the technology , nor is it optimal even as an introduction to the philosophical problems in interpreting Bayesian networks . Rather Williamson's book is an attempt to move the debate forward by solving the central problems of the …. (shrink)

This paper argues against Papineau's claim that causal relations can be reduced to correlations and defends Cartwright's thesis that they can be nevertheless boot-strapped from them, given sufficiently rich causal background knowledge.

For a long time, regularity accounts of causation have virtually vanished from the scene. Problems encountered within other theoretical frameworks have recently induced authors working on causation, laws of nature, or methodologies of causal reasoning – as e.g. May (Kausales Schliessen. Eine Untersuchung über kausale Erklärungen und Theorienbildung. Ph.D. thesis, Universität Hamburg, Hamburg, 1999), Ragin (Fuzzy-set social science. Chicago: University of Chicago Press, 2000), Graßhoff and May (Causal regularities. In W. Spohn, M. Ledwig, & M. Esfeld (Eds.), Current issues in (...) causation (pp. 85–114). Paderborn: Mentis, 2001), Swartz (The concept of physical law (2nd ed.). http://www.sfu.ca/philosophy/physical-law/, 2003), Halpin (Erkenntnis, 58, 137–168, 2003) – to direct their attention back to regularity theoretic analyses. In light of the latest proposals of regularity theories, the paper at hand therefore reassesses the criticism raised against regularity accounts since the INUS theory of causation of Mackie (The cement of the universe. A study of causation. Oxford: Clarendon Press, 1974). It is shown that most of these objections target strikingly over-simplified regularity theoretic sketches. By outlining ways to refute these objections it is argued that the prevalent conviction as to the overall failure of regularity theories has been hasty. (shrink)

En un libro reciente McShea y Brandon defienden que la diversidad y la complejidad de la vida se explican, principalmente, por la acción de un principio que llaman “la ley evolutiva de fuerzas cero” o “ZFEL”. Tal principio actuaría de un modo implícito por detrás de muchas explicaciones de la biología, pero nunca habría sido explicitado. Asumiendo que esta idea es interesante, y que los autores en cuestión tienen razón, discutiremos el modo metateórico en que presentan dicho principio, como siendo (...) parte de la teoría de la probabilidad. Esto permite a los autores afirmar que la teoría de la probabilidad brindaría la base reductiva para toda la biología evolutiva (dado que consideran que otros principios, como el de selección natural, también serían parte de la teoría de la probabilidad). Defenderemos que, efectivamente, ZFEL no es propio de la biología únicamente, pero no por formar parte de la teoría de la probabilidad, sino por tratarse de una versión específica del principio de causa común. -/- In a recent book, McShea and Brandon argue that the observed diversity and complexity of life are explainable by a principle they call the “zero-force evolutionary law” or “ZFEL”. Although this principle would be implicit in many explanations given by biologists, it would have never been made explicit. Assuming that this idea is interesting, and that the authors are right, we will discuss the metatheoretical way in which they present said principle, as being a part of probability theory. This allows the authors to claim that probability theory provides the reductive basis for all evolutionary biology. We will defend, in accordance with them, that ZFEL is not a solely biological principle, but not because it is a part of probability theory, but rather because it is a specific version of the principle of common cause. (shrink)

In his recent paper in the Journal of Economic Methodology, Tobias Henschen puts forth a manipulationist definition of macroeconomic causality that strives for adequacy. As the notion of ‘adequacy’...

This paper discusses two challenges for a Boolean method for establishing constitutive regularity statements which, according to the regularity theory of mechanistic constitution, form the core of any mechanistic explanation in neuroscience. After presenting the regularity definition for the constitution relation and a methodology for constitutive inference, the paper discusses the problem of full variation of tested mechanistic factors and the problem of informational redundancy. A solution is offered for each problem. The first requires some adjustments to the original theory (...) by introducing the technical notion of a set of types satisfying independent instantiability. The second one is resolved by demonstrating that the problem of informational redundancy is based on a confusion that fails to challenge the theory. It is concluded that the methodology of constitutive inference is consistent and plausible with respect to actual practice in neuroscience. (shrink)

Identifying causal relations from correlational data is a fundamental challenge in personality psychology. In most cases, random assignment is not feasible, leaving observational studies as the primary methodological tool. Here, we document several techniques from behavior genetics that attempt to demonstrate causality. Although no one method is conclusive at ruling out all possible confounds, combining techniques can triangulate on causal relations. Behavior genetic tools leverage information gained by sampling pairs of individuals with assumed genetic and environmental relatedness or by measuring (...) genetic variants in unrelated individuals. These designs can find evidence consistent with causality, while simultaneously providing strong controls against common confounds. We conclude by discussing several potential problems that may limit the utility of these techniques when applied to personality. Ultimately, genetically informative designs can aid in drawing causal conclusions from correlational studies. (shrink)

Data from medical research are typically summarized with various types of outcome measures. We present three arguments in favor of absolute over relative outcome measures. The first argument is from cognitive bias: relative measures promote the reference class fallacy and the overestimation of treatment effectiveness. The second argument is decision-theoretic: absolute measures are superior to relative measures for making a decision between interventions. The third argument is causal: interpreted as measures of causal strength, absolute measures satisfy a set of desirable (...) properties, but relative measures do not. Absolute outcome measures outperform relative measures on all counts. (shrink)

The principle of the common cause claims that if an improbable coincidence has occurred, there must exist a common cause. This is generally taken to mean that positive correlations between non-causally related events should disappear when conditioning on the action of some underlying common cause. The extended interpretation of the principle, by contrast, urges that common causes should be called for in order to explain positive deviations between the estimated correlation of two events and the expected value of their correlation. (...) The aim of this paper is to provide the extended reading of the principle with a general probabilistic model, capturing the simultaneous action of a system of multiple common causes. To this end, two distinct models are elaborated, and the necessary and sufficient conditions for their existence are determined. (shrink)

ABSTRACT Joseph Halpern and Judea Pearl draw upon structural equation models to develop an attractive analysis of ‘actual cause’. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation. 1Introduction 2Preemption 3Structural Equation Models 4The Halpern and Pearl Definition of ‘Actual Cause’ 5Preemption Again 6The Probabilistic Case 7Probabilistic Causal Models 8A Proposed Probabilistic Extension of Halpern and Pearl’s Definition 9Twardy and Korb’s Account 10Probabilistic (...) Fizzling 11Conclusion. (shrink)

In der vorliegenden Arbeit soll ein in der analytischen Philosophie aufgestelltes Programm zur Explikation der kausalen Begrifflichkeit kritisch untersucht werden. Seine kritische Analyse mag im Hinblick auf die Rolle von Interesse sein, welche die kausalen Begriffe in einer allgemeinen Theorie der wissenschaftlichen Erklärung zu spielen haben. Diese Analyse könnte außerdem m.E. zur Bestimmung des Ausgangspunktes einer positiven Behandlung der mit den kausalen Begriffen zusammenhängenden Sinnfragen beitragen.

Not only has the philosophical debate on causation been gaining ground in the last few decades, but it has also increasingly addressed the sciences. The biomedical sciences are among the most prominent fields that have been considered, with a number of works tackling the understanding of the notion of cause, the assessment of genuinely causal relations and the use of causal knowledge in applied contexts. Far from denying the merits of the debate on causation and the major theories it comprises, (...) this paper is meant as a stimulus for theorists of causation in the philosophy of biomedicine, with a focus on clinical matters. Without aiming at putting forward an original theory of causation and starting from the narration of two actual but paradigmatic cases at the joints between biomedical research and clinical practice, we want to point out that some pathological situations addressed by molecular medicine actually prove resistant to some of our major epistemological accounts of causal explanation. Given this scenario, which is very frequent in our hospitals, our analysis aims to provide a stimulus for the debate among theorists of causation in biomedicine interested in real cases in science in practice. We believe that this might in turn encourage some more general rethinking of the complex intertwinement of science, philosophy of science and ethics, as well as of the role of philosophy of science for clinical medicine itself. (shrink)

Modular approaches to the architecture of the mind claim that some mental mechanisms, such as sensory input processes, operate in special-purpose subsystems that are functionally independent from the rest of the mind. This assumption of modularity seems to be in tension with recent claims that the mind has a predictive architecture. Predictive approaches propose that both sensory processing and higher-level processing are part of the same Bayesian information-processing hierarchy, with no clear boundary between perception and cognition. Furthermore, it is not (...) clear how any part of the predictive architecture could be functionally independent, given that each level of the hierarchy is influenced by the level above. Both the assumption of continuity across the predictive architecture and the seeming non-isolability of parts of the predictive architecture seem to be at odds with the modular approach. I explore and ultimately reject the predictive approach’s apparent commitments to continuity and non-isolation. I argue that predictive architectures can be modular architectures, and that we should in fact expect predictive architectures to exhibit some form of modularity. (shrink)

Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in existing studies. (...) More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation. (shrink)

Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly (...) through a detailed examination of actual examples of causal notions in physics, including causal principles invoked in linear response theory and in representations of radiation phenomena. Offering a new perspective on the nature of scientific theories and causal reasoning, this book will be of interest to professional philosophers, graduate students, and anyone interested in the role of causal thinking in science. (shrink)

More than a century ago, Russell launched a forceful attack on causation, arguing not only that modern physics has no need for causal notions but also that our belief in causation is a relic of a pre-scientific view of the world. He thereby initiated a debate about the relations between physics and causation that remains very much alive today. While virtually everybody nowadays rejects Russell's causal eliminativism, many philosophers have been convinced by Russell that the fundamental physical structure of our (...) world doesn't contain causal relations. This raises the question of how to reconcile the central role of causal concepts in the special sciences and in common sense with the putative absence of causation in fundamental physics. (shrink)

This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...) adequacy conditions. The paper concludes by sketching future research avenues. (shrink)

Several philosophers have embraced the view that high-level events—events like Zimbabwe's monetary policy and its hyper-inflation—are causally related if their corresponding low-level, fundamental physical events are causally related. I dub the view which denies this without denying that high-level events are ever causally related causal emergentism. Several extant philosophical theories of causality entail causal emergentism, while others are inconsistent with the thesis. I illustrate this with David Lewis's two theories of causation, one of which entails causal emergentism, the other of (...) which entails its negation. I then argue for causal emergentism on the grounds that it provides the only adequate means of squaring the apparent plenitude of causal relations between low-level events with the apparent scarcity of causal relations between high-level events. This tension between the apparent abundance of low-level causation and the apparent scarcity of high-level causation has been noted before. However, it has been thought that various theses about the semantics or the pragmatics of causal claims could be used to ameliorate the tension without going in for causal emergentism. I argue that none of the suggested semantic or pragmatic strategies meet with success, and recommend emergentist theories of causality in their stead. As Lewis's 1973 account illustrates, causal emergentism is consistent with the thesis that all facts reduce to microphysical facts. (shrink)

Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which (...) inferences (e.g., diagnoses and prognoses) are appropriate, rather than about the presence of some physical causal relation analogous to distance or gravitational attraction. It is shown that the epistemic theory has important consequences for medical practice, in particular with regard to evidence-based causal assessment. (shrink)

This paper outlines a theory and computer implementation of causal meanings and reasoning. The meanings depend on possibilities, and there are four weak causal relations: A causes B, A prevents B, A allows B, and A allows not‐B, and two stronger relations of cause and prevention. Thus, A causes B corresponds to three possibilities: A and B, not‐A and B, and not‐A and not‐B, with the temporal constraint that B does not precede A; and the stronger relation conveys only the (...) first and last of these possibilities. Individuals represent these relations in mental models of what is true in the various possibilities. The theory predicts a number of phenomena, and, contrary to many accounts, it implies that the meaning of causation is not probabilistic, differs from the meaning of enabling conditions, and does not depend on causal powers or mechanisms. The theory also implies that causal deductions do not depend on schemas or rules. (shrink)

Probability ratio and likelihood ratio measures of inductive support and related notions have appeared as theoretical tools for probabilistic approaches in the philosophy of science, the psychology of reasoning, and artificial intelligence. In an effort of conceptual clarification, several authors have pursued axiomatic foundations for these two families of measures. Such results have been criticized, however, as relying on unduly demanding or poorly motivated mathematical assumptions. We provide two novel theorems showing that probability ratio and likelihood ratio measures can be (...) axiomatized in a way that overcomes these difficulties. (shrink)

This contribution claims that the two fundamental notions of causation at work in the health sciences are manipulative and mechanistic, and investigates what kinds of evidence matter for the assessment of causal relations. This article is a development of our 2007 article, ‘Plurality of Causality’, where we argue for a pluralistic account of causation with an eye to econometrics and a single medical example. The present contribution has a wider focus, and considers the notion of evidence within a whole range (...) of disciplines belonging to the health sciences. Section 1 addresses the relations between kinds of evidence and causal accounts, and it is shown how different notions of causation can be employed in various medical cases. Section 2 calls attention to issues crucial for any adequate epistemological theory of causation, such as the distinctions between types and tokens, observational and experimental regimes, explanation and prediction. Lastly, the notion of context is articulated, highlighted in its role in the assessment of causal links. All these issues are tackled in the framework of what we label a ‘bottom–up’ epistemology. (shrink)

This essay is concerned with concentrations of entities, which play an important—albeit often overlooked—role in scientific explanation. First, I discuss an example from molecular biology to show that concentrations can play an irreducible causal role. Second, I provide a preliminary philosophical analysis of this causal role, suggesting some implications for extant theories of causation. I conclude by introducing the concept of causation by concentration, a form of statistical causation whose widespread presence throughout the sciences has been unduly neglected and which (...) deserves to be studied in more depth. 1 Introduction2 Solving Lillie's Paradox: Lysogenic Induction in Phage λ3 Repressor Concentration and the Tuning of the Switch4 Concentration and Causality5 Preemption in Concentrations: Analysis and Implications6 Causation by Concentration: General Definition, Refinements, and Further Applications. (shrink)

In this paper, using a multilevel approach, we defend the positive role of natural selection in the generation of organismal form. Despite the currently widespread opinion that natural selection only plays a negative role in the evolution of form, we argue, in contrast, that the Darwinian factor is a crucial (but not exclusive) factor in morphological organization. Analyzing some classic arguments, we propose incorporating the notion of ‘downward causation’ into the concept of ‘natural selection.’ In our opinion, this kind of (...) causation is fundamental to the operation of selection as a creative evolutionary process. (shrink)

Backtracking influence is influence that zigzags in time. For example, backtracking influence exists when an event E_1 makes an event E_2 more likely by way of a nomic connection that goes from E_1 back in time to an event C and then forward in time to E_2. I contend that in our local region of spacetime, at least, backtracking influence is redundant in the sense that any existing backtracking influence exerted by E_1 on E_2 is equivalent to E_1's temporally direct (...) influence on E_2. I prove the redundancy of backtracking influence using several plausible physical principles without assuming any fundamental temporal or causal asymmetry. This explanation can play a prominent role in an account of why causation appears to be objectively asymmetric regardless of whether the fundamental laws are symmetric. (shrink)

The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...) an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses. (shrink)

Most contemporary political science researchers are advocates of multimethod research, however, the value and proper role of qualitative methodologies, like case study analysis, is disputed. A pluralistic philosophy of science can shed light on this debate. Methodological pluralism is indeed valuable, but does not entail causal pluralism. Pluralism about the goals of science is relevant to the debate and suggests a focus on the difference between evidence for warrant and evidence for use. I propose that case study research provides evidence (...) for use through providing information that bears on the applicability of causal generalizations and risk assessment. (shrink)

It is shown that, given any finite set of pairs of random events in a Boolean algebra which are correlated with respect to a fixed probability measure on the algebra, the algebra can be extended in such a way that the extension contains events that can be regarded as common causes of the correlations in the sense of Reichenbach's definition of common cause. It is shown, further, that, given any quantum probability space and any set of commuting events in it (...) which are correlated with respect to a fixed quantum state, the quantum probability space can be extended in such a way that the extension contains common causes of all the selected correlations, where common cause is again taken in the sense of Reichenbach's definition. It is argued that these results very strongly restrict the possible ways of disproving Reichenbach's Common Cause Principle. (shrink)

Critics of functional explanations in social science maintain that such explanations are illegitimate unless a mechanism is specified. Others argue that mechanisms are not necessary for causal inference and that functional explanations are a type of causal claim that raise no special difficulties for testing. I show that there is indeed a special problem that confronts testing functional explanations resulting from their connection to second-order causal claims. I explain how mechanisms can resolve this difficulty, but argue that this does not (...) provide support for methodological individualism since it is not necessary that the mechanisms be described in terms of individual interactions. (shrink)

The paper displays the similarity between the theory of probabilistic causation developed by Glymour et al. since 1983 and mine developed since 1976: the core of both is that causal graphs are Bayesian nets. The similarity extends to the treatment of actions or interventions in the two theories. But there is also a crucial difference. Glymour et al. take causal dependencies as primitive and argue them to behave like Bayesian nets under wide circumstances. By contrast, I argue the behavior of (...) Bayesian nets to be ultimately the defining characteristic of causal dependence. (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)

The notion of ‘causal web’ emerged in the epidemiological literature in the early Sixties and had to wait until the Nineties for a thorough critical appraisal. Famously, Nancy Krieger argued that such a notion isn’t helpful unless we specify what kind of spiders create the webs. This means, according to Krieger, (i) that the role of the spiders is to provide an explanation of the yarns of the web and (ii) that the sought spiders have to be biological and social. (...) This paper contributes to the development of the notion of causal web, elaborating on the two following points: (i) to catch the spiders we need multi-fold evidence—specifically, mechanistic and difference-making—and (ii) for the eco-social to be explanatory, the web has to be mechanistic in a sense to be specified. (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)