The problem of collecting, analyzing and evaluating evidence on adverse drug reactions (ADRs) is an example of the more general class of epistemological problems related to scientific inference and prediction, as well as a central problem of the health-care practice. Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with (...) a special attention to the privileged role assigned to randomized controlled trials in Evidence Based Medicine. Regardless of whether the RCT’s privilege holds for efficacy assessment, it is nevertheless important to make a distinction between causal inference of intended and unintended effects, in that the unknowns at stake are heterogonous in the two contexts. This point has been emphasized by epidemiologists in the last decade. Their main focus is methodological, and regards the fact that bias and confounding do not affect studies on intended and unintended effects in the same way. However, deeper concerns ground the intuition for such a distinction; these are related to the constraints which we impose on evidence and their epistemological justification. My thesis is that such constraints ought to be understood to be different in the case of evidence for risk vs. benefit assessment. I present the recent debate on the causal association between acetaminophen and asthma in order to illustrate the point at issue. (shrink)

The problem of collecting, analyzing and evaluating evidence on adverse drug reactions (ADRs) is an example of the more general class of epistemological problems related to scientific inference and prediction, as well as a central problem of the health-care practice. Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with (...) a special attention to the privileged role assigned to randomized controlled trials in Evidence Based Medicine. Regardless of whether the RCT’s privilege holds for efficacy assessment, it is nevertheless important to make a distinction between causal inference of intended and unintended effects, in that the unknowns at stake are heterogonous in the two contexts. This point has been emphasized by epidemiologists in the last decade. Their main focus is methodological, and regards the fact that bias and confounding do not affect studies on intended and unintended effects in the same way. However, deeper concerns ground the intuition for such a distinction; these are related to the constraints which we impose on evidence and their epistemological justification. My thesis is that such constraints ought to be understood to be different in the case of evidence for risk vs. benefit assessment. I present the recent debate on the causal association between acetaminophen and asthma in order to illustrate the point at issue. (shrink)

Despite being a milestone in the history of statistical causal inference, Sewall Wright’s 1918 invention of path analysis did not receive much immediate attention from the statistical and scientific community. Through a careful historical analysis, this paper reveals some previously overlooked philosophical issues concerning the history of causal inference. Placing the invention of path analysis in a broader historical and intellectual context, I portray the scientific community’s initial lack of interest in the method as a natural (...) consequence of relevant scientific and philosophical conditions. In addition to Karl Pearson’s positivist refutation of causation, I contend that the acceptance of path analysis faced several other challenges, including the introduction of a new formalism, conceptual barriers to causal inference, and the lack of model-based statistical thinking. The presence of these challenges shows that the delayed progress in causal inference in the early twentieth century was inevitable. (shrink)

Semmelweis’s discovery of the cause of puerperal fever around the middle of the 19th century counts among the paradigm cases of scientific discovery. For several decades, philosophers of science have used the episode to illustrate, appraise and compare views of proper scientific methodology.Here I argue that the episode can be profitably reexamined in light of two cognate notions: causal reasoning and mechanisms. Semmelweis used several causal reasoning strategies both to support his own and to reject competing (...) hypotheses. However, these strategies have gone unappreciated in the existing literature. I show that a causal reasoning approach makes sense of the multitude of tables in Semmelweis’s main text, which in later editions were often abridged because they appeared redundant.Moreover, the existing literature tends to focus on Semmelweis’s clinical intervention and on the extent to which it alone confirms his theoretical conclusions. This neglects Semmelweis’s efforts to show by animal experiments that his clinical results are in agreement with a demonstrable mechanism of puerperal fever pathogenesis. I argue that the full evidential force of Semmelweis’s argument can only be appreciated if both his clinical and his laboratory investigations are taken into account. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal (...) inference, and an inductive rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

Causality is central to the understanding and use of data; without an understanding of cause and effect relationships, we cannot use data to answer important questions in medicine and many other fields.

As is well known, the Humean account of causal inference gives a central location to inference habits. Some of these habits one can discipline. Thus, one can so discipline oneself as to reason in accordance with the “rules by which to judge of causes and effects”, that is, one can discipline oneself to think scientifically, rather than, say, in accordance with the rules of prejudice, or of superstition. All such judgments, even those of science, are, however, upon the Humean (...) account fallible. Some, or rather very many, have raised the issue about how any such causal judgment or inference is justified. That is, how can such inference habits be justified, shown to be reasonable. Hume meets this challenge by arguing that there are some habits all men do and must have. Because we must have them we ought to have them. They are, therefore, justified. (shrink)

The paper has two aims. First, to show that we need social mechanisms to establish the policy relevance of causal claims, even if it is possible to build a good argument for those claims without knowledge of mechanisms. Second, to show that although social scientists can, in principle, do without social mechanisms when they argue for causal claims, in reality scientific practice contexts where they do not need mechanisms are very rare. Key Words: social mechanisms • (...) class='Hi'>causal inference • social policy. (shrink)

The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, (...) agreeing measurements of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation. (shrink)

The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, (...) agreeing measurements of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation. (shrink)

The aim of Fred Wilson’s detailed treatment of Hume’s theory of causal inference is to show that although Hume posed a skeptical challenge concerning the justification of beliefs through causal inference, Hume also presents a solution to that challenge, a solution that includes a justification of causal inference. Wilson’s Hume is not a skeptic, but rather a vindicator of the norms of scientific inquiry. This study is wide-ranging, both in terms of the texts Wilson interprets and (...) the connections he draws to the views of other philosophers, both modern and contemporary. (shrink)

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures (...) the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera. (shrink)

We hear that a glass of red wine prolongs life, that alcohol is a carcinogen, that pregnant women should drink not a drop of alcohol. Major medical journals first claimed that hormone replacement therapy reduces the risk of heart disease, then reversed themselves and said it increases the risk of heart disease. What are the effects caused by consuming alcohol or by receiving hormone replacement therapy? These are causal questions, questions about the effects caused by treatments, policies or preventable (...) exposures. Some causal questions can be studied in randomized trials, in which a coin is flipped to decide the treatment for the next experimental subject. Because randomized trials are not always practical, nor always ethical, many causal questions are investigated in non-randomized observational studies. The reversal of opinion about hormone replacement therapy occurred when a randomized clinical trial contradicted a series of earlier observational studies. Using minimal mathematics - high school algebra and coin flips -- and numerous examples, Observation and Experiment explains the key concepts and methods of causal inference. Examples of randomized experiments and observational studies are drawn from clinical medicine, economics, public health and epidemiology, clinical psychology and psychiatry. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Hume's Defence of Causal InferenceDon GarrettFred Wilson. Hume's Defence of Causal Inference. Toronto: University of Toronto Press, 1997. Pp. xii + 439. Cloth, $80.00.According to its introduction, this book "deals solely with the problem of induction [and] solely with the issue of whether Hume is a sceptic with regard to causation and scientific reason" (p. 6). Wilson concludes that although Hume rejects "objective" necessary connections, (...) he is not a sceptic—or at least, no more than a fallibilist "academic" sceptic—about induction. In contrast to several relatively recent commentators, he accepts the traditional view that Hume did pose a general sceptical problem about induction. One of Wilson's many formulations of that problem (111) is this: "How can [inductive] inference habits be justified, shown to be reasonable?" According to Wilson, Hume raised this problem and offered a solution to it. In fact, he holds, Hume provided at least the outlines of the correct solution.The book consists of just three chapters, but each chapter is over one hundred pages long. Not surprisingly, then, it is not really devoted solely to the question of whether Hume was or was not a sceptic about induction. In fact, the book takes up a wide variety of other related topics, incorporating considerable amounts of material [End Page 126] from no fewer than thirteen of Wilson's previously-published articles. It helpfully formalizes arguments wherever possible.The first chapter is a series of set-pieces intended to prepare the way for more tightly focused discussion of Hume's treatment of inductive scepticism. Topics include Hume's two definitions of 'cause,' necessary connections, dispositions, mental activity, the epistemology of scientific instruments, and Hume's treatment of "scepticism with regard to the senses." One recurring theme is that Hume is no proto-Kantian about mental activity or the universality of causation; another is the importance to Hume of his "Rules by which to judge of causes and effects."The second chapter is the centerpiece of the book, for it is here that Wilson tries to show how Hume justifies inductive scientific reasoning. The justification, as Wilson reconstructs it, has two steps. The first step is to argue that we ought to make inductive inferences on the grounds that (as a matter of psychological necessity) we must make such inferences. But this is only half the battle, since there are various ways to make inductive inferences and various strategies and influences that might or might not be allowed to affect the outcome. Hume's chosen way of making inductive inferences, Wilson argues, is the scientific method of "eliminative induction," as embodied in the "Rules by which to judge of causes and effects." Hume's strategy for justifying this method, according to Wilson, is to show that it is "reasonable" by showing that it is the method best suited for a particular practical purpose—namely, the purpose of satisfying our passion of "curiosity." This passion aims not only at the pleasure of exercising our faculties, but also at the achievement of truth, or at least (what seems rather different) of a system of beliefs that will prove "satisfactory to the mind." Accordingly, Wilson takes Hume to argue that using the method of eliminative induction embodied in the "Rules" constitutes "the best we can do" by way of discovering the truth of generalizations and that experience shows other methods to be less satisfactory in attaining the truth. Of course, a method's being the best we can do does not in itself entail that it is good enough to warrant adoption; and to treat the past successes or failures of a particular method as indicative of its future success or failure is to rely on induction itself. Presumably, then, Wilson's Hume is meant to regard the inductive arguments showing the "reasonableness" of the "Rules" as themselves among the inductions that any human being ultimately must accept, regardless of specific methods, strategies, or influences.The book's third and final chapter takes up the challenge posed by the section of Hume's Treatise of Human Nature entitled "Of scepticism with regard to reason," in which Hume argues that "all the rules of logic... (shrink)

The claims of RCTs to be the gold standard rest on the fact that the ideal RCT is a deductive method: if the assumptions of the test are met, a positive result implies the appropriate causal conclusion. This is a feature that RCTs share with a variety of other methods, which thus have equal claim to being a gold standard. This paper describes some of these other deductive methods and also some useful non-deductive methods, including the hypothetico-deductive method. It (...) argues that with all deductive methods, the benefit that the conclusions follow deductively in the ideal case comes with a great cost: narrowness of scope. This is an instance of the familiar trade-off between internal and external validity. RCTs have high internal validity but the formal methodology puts severe constraints on the assumptions a target population must meet to justify exporting a conclusion from the test population to the target. The paper reviews one such set of assumptions to show the kind of knowledge required. The overall conclusion is that to draw causal inferences about a target population, which method is best depends case-by-case on what background knowledge we have or can come to obtain. There is no gold standard. (shrink)

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. (...) The network uses spike-time dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla–Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions. (shrink)

The claims of RCTs to be the gold standard rest on the fact that the ideal RCT is a deductive method: if the assumptions of the test are met, a positive result implies the appropriate causal conclusion. This is a feature that RCTs share with a variety of other methods, which thus have equal claim to being a gold standard. This paper describes some of these other deductive methods and also some useful non-deductive methods, including the hypothetico-deductive method. It (...) argues that with all deductive methods, the benefit that the conclusions follow deductively in the ideal case comes with a great cost: narrowness of scope. This is an instance of the familiar trade-off between internal and external validity. RCTs have high internal validity but the formal methodology puts severe constraints on the assumptions a target population must meet to justify exporting a conclusion from the test population to the target. The paper reviews one such set of assumptions to show the kind of knowledge required. The overall conclusion is that to draw causal inferences about a target population, which method is best depends case-by-case on what background knowledge we have or can come to obtain. There is no gold standard. (shrink)

The claims of randomized controlled trials to be the gold standard rest on the fact that the ideal RCT is a deductive method: if the assumptions of the test are met, a positive result implies the appropriate causal conclusion. This is a feature that RCTs share with a variety of other methods, which thus have equal claim to being a gold standard. This article describes some of these other deductive methods and also some useful non-deductive methods, including the hypothetico-deductive (...) method. It argues that with all deductive methods, the benefit that the conclusions follow deductively in the ideal case comes with a great cost: narrowness of scope. This is an instance of the familiar trade-off between internal and external validity. RCTs have high internal validity but the formal methodology puts severe constraints on the assumptions a target population must meet to justify exporting a conclusion from the test population to the target. The article reviews one such set of assumptions to show the kind of knowledge required. The overall conclusion is that to draw causal inferences about a target population, which method is best depends case-by-case on what background knowledge we have or can come to obtain. There is no gold standard. (shrink)

Randomized controlled trials sometimes test interventions that aim to improve existing services targeted to a subset of individuals identified after randomization. Accordingly, the treatment could affect the composition of service recipients and the offered services. With such bias, intention-to-treat estimates using data on service recipients and nonrecipients may be difficult to interpret. This article develops causal estimands and inverse probability weighting estimators for complier populations in these settings, using a generalized estimating equation approach that adjusts the standard errors for (...) estimation error in the IPW weights. While our focus is on more general clustered RCTs, the methods also apply to nonclustered RCTs. Simulations show that the estimators achieve nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from a large-scale RCT testing the effects of early childhood services on children’s cognitive development scores. An R program for estimation is available for download. (shrink)

Machine learning is used more and more in scientific contexts, from the recent breakthroughs with AlphaFold2 in protein fold prediction to the use of ML in parametrization for large climate/astronomy models. Yet it is unclear whether we can obtain scientific explanations from such models. I argue that when machine learning is used to conduct causal inference we can give a new positive answer to this question. However, these ML models are purpose-built models and there are technical results (...) showing that standard machine learning models cannot be used for the same type of causal inference. Instead, there is a pathway to causal explanations from predictive ML models through new explainability techniques; specifically, new methods to extract structural equation models from such ML models. The extracted models are likely to suffer from issues though: they will often fail to account for confounders and colliders, as well as deliver simply incorrect causal graphs due to ML models tendency to violate physical laws such as the conservation of energy. In this case, extracted graphs are a starting point for new explanations, but predictive accuracy is no guarantee for good explanations. (shrink)

The thesis of underdetermination presents a major obstacle to the epistemological claims of scientific realism. That thesis is regularly assumed in the philosophy of science, but is puzzlingly at odds with the actual history of science, in which empirically adequate theories are thin on the ground. We propose to advance a case for scientific realism which concentrates on the process of scientific reasoning rather than its theoretical products. Developing an account of causal–explanatory inference will make it (...) easier to resist the thesis of underdetermination. For, if we are not restricted to inference to the best explanation only at the level of major theories, we will be able to acknowledge that there is a structure in data sets which imposes serious constraints on possible theoretical alternatives. We describe how Differential Inference, a form of inference based on contrastive explanation, can be used in order to generate causal hypotheses. We then go on to consider how experimental manipulation of differences can be used to achieve Difference Closure, thereby confirming claims of causal efficacy and also eliminating possible confounds. The model of Differential Inference outlined here shows at least one way in which it is possible to ‘reason from the phenomena’. (shrink)

In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Kitcher’s unificationist account supports IBE; Railton’s deductive–nomological–probabilistic model, Salmon’s statistical-relevance model, and van Fraassen’s erotetic (...) account are incompatible with IBE; and Salmon’s causal–mechanical model is merely consistent with IBE. In short, many influential models of scientific explanation do not support IBE. I end by outlining three possible conclusions to draw: (1) either philosophers of science or defenders of IBE have seriously misconstrued the concept of explanation, (2) philosophers of science and defenders of IBE do not use the term ‘explanation’ univocally, and (3) the ampliative conception of IBE, which is compatible with any model of scientific explanation, deserves a closer look. (shrink)

It is widely believed that many of the competing accounts of scientific explanation have ramifications which are relevant to the scientific realism debate. I claim that the two issues are orthogonal. For definiteness, I consider Cartwright's argument that causal explanations secure belief in theoretical entities. In Section I, van Fraassen's anti-realism is reviewed; I argue that this anti-realism is, prima facie, consistent with a causal account of explanation. Section II reviews Cartwright's arguments. In Section III, it (...) is argued that causal explanations do not license the sort of inferences to theoretical entities that would embarass the anti-realist. Section IV examines the epistemic commitments involved in accepting a causal explanation. Section V presents my conclusions: contra Cartwright, the anti-realist may incorporate a causal account of explanation into his vision of science in an entirely natural way. (shrink)

Explanationist strategies for defending epistemological scientific realism make heavy use of a particular version of inference to the best explanation known as the no-miracle argument. I consider ESR to be a genuinely philosophical—non-naturalistic—thesis which contends that there are strong arguments to believe in some non-observational claims made by scientific theories that are partially observationally correct. In this paper, I examine the grounds of the strength of these arguments from what I call a contemplative perspective which focuses on the (...) end products, i.e. theories, of the scientific activity as opposed to the pragmatist view which considers science to be primarily an activity. I briefly rehearse the main difficulties of the no-miracle argument and of inference to the best explanation in general. I argue that a convincing defence of ESR should be based on the empirically ascertained reality of causal connections between theoretical entities which possess properties that are in principle observable and the results of measurements or observations. The knowledge of those causal connections may well deliver an—even the best—explanation of the appearances. But belief in the existence of some unobservable entities is mainly justified by their empirically attested causal role, not on their possible explanatory function. (shrink)

The past century has witnessed numerous methodological innovations in probabilistic and statistical methods of causal inference (e.g., the graphical modelling and the potential outcomes frameworks, as introduced in Chapter 1). These innovations have not only enhanced the methodologies by which scientists across diverse domains make causal inference, but they have also made a profound impact on the way philosophers think about causation. The philosophical issues discussed in this thesis are stimulated and inspired by these methodological innovations. Chapter 2 (...) addresses the question of how the holding of screening-off conditions for a causal model depends on the choice of variables. As bridge principles between probability and causation, screening-off conditions (especially the Causal Markov Condition) play a key role in causal inference. However, it has been known that these conditions may fail due to poor variable choice. My aim in this chapter is to further examine those constraints on variable choice that are deemed necessary for the satisfaction of screening-off conditions. The idea of a well-defined (hypothetical) intervention is also crucial for reliable causal inference. Chapter 3 explores the question of when interventions invoked in causal inference are “well-defined” or unambiguous, and how this requirement constrains the choice of cause-variables. I propose that an intervention is well-defined just in case the effect of interest is well-defined (under ideal interventions), and that the intervention can serve as a suitable means to identify that effect. Based on this proposal, several distinct types of ambiguous interventions are identified. Methodological progress in causal inference also poses the following question: Can such progress shed light on the ontology of causation? My answer is yes. In Chapter 4, I develop a Carnapian-pragmatist approach to the ontology of causation as an alternative to existing metaphysical approaches. I argue that, compared to traditional metaphysics, the pragmatist approach provides a superior picture of how the ontology and methodology of causation interact with each other in scientific practice. I conclude in Chapter 5 that the thing we call “causation” consists in both the right worldly infrastructure (e.g., screening-off patterns and possibilities for interventions) and appropriate ways of framing this infrastructure. (shrink)

Recent debates over the nature of causation, casual inference, and the uses of causal models in counterfactual analysis, involving inter alia Nancy Cartwright (Hunting Causes and Using Them), James Woodward (Making Things Happen), and Judea Pearl (Causation), hinge on how causality is represented in models. Economists’ indigenous approach to causal representation goes back to the work of Herbert Simon with the Cowles Commission in the early 1950s. The paper explicates a scheme for the representation of causal structure, (...) inspired by Simon, and shows how this representation sheds light on some important debates in the philosophy of causation. This structural account is compared to Woodward’s manipulability account. It is used to evaluate the recent debates – particularly, with respect to the nature of causal structure, the identity of causes, causal independence, and modularity. Special attention is given to modeling issues that arise in empirical economics. (shrink)

An important function of scientific diagrams is to identify causal relationships. This commonly relies on contrasts that highlight the effects of specific difference-makers. However, causal contrast diagrams are not an obvious and easy to recognize category because they appear in many guises. In this paper, four case studies are presented to examine how causal contrast diagrams appear in a wide range of scientific reports, from experimental to observational and even purely theoretical studies. It is shown (...) that causal contrasts can be expressed in starkly different formats, including photographs of complexly visualized macromolecules as well as line graphs, bar graphs, or plots of state spaces. Despite surface differences, however, there is a measure of conceptual unity among such diagrams. In empirical studies they generally serve not only to infer and communicate specific causal claims, but also as evidence for them. The key data of some studies is given nowhere except in the diagrams. Many diagrams show multiple causal contrasts in order to demonstrate both that an effect exists and that the effect is specific -- that is, to narrowly circumscribe the phenomenon to be explained. In a large range of scientific reports, causal contrast diagrams reflect the core epistemic claims of the researchers. (shrink)

The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful (...) motivation for choosing the strategy of modeling. Second, we show that the development of the model follows a trajectory from a “how possibly” to a “how actually” model. We discuss how and to what extent Volterra and D’Ancona were able to advance their model along that trajectory. It turns out they were unable to establish that their model was fully applicable to any system. Third, we consider another instance of model-based science: Darwin’s model of the origin and distribution of coral atolls in the Pacific Ocean. Darwin argued more successfully that his model faithfully represents the causal structure of the target system, and hence that it is a “how actually” model. (shrink)

While scientific realism generally assumes that successful scientific explanations yield information about reality, realists also have to admit that not all information acquired in this way is equally well warranted. Some versions of scientific realism do this by saying that explanatory posits with which we have established some kind of causal contact are better warranted than those that merely appear in theoretical hypotheses. I first explicate this distinction by considering some general criteria that permit us to (...) distinguish causal warrant from theoretical warrant. I then apply these criteria to a specific case from particle physics, claiming that scientific realism has to incorporate the distinction between causal and theoretical warrant if it is to be an adequate stance in the philosophy of particle physics. (shrink)

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. (...) In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects. (shrink)

Grünbaum argues that psychoanalysis cannot justify its inferences regarding motives using its own methodology, as only the employment of Mill’s canons can justify causal inferences (which inferences to motives are). I consider an argument offered by Hopkins regarding the nature and status of our everyday inferences from other people’s behavior to their motives that seeks to rebut Grünbaum’s charge by defending a form of inference to the best explanation that makes use of connections in intentional content between behavior and (...) motives. I argue that Hopkins succeeds in defeating Grünbaum’s objection as it is presented, but that work in social psychology presents a further challenge. I discuss the extent to which the challenge can be met, and conclude that certain types of inference in psychoanalysis are justifiable, but others, including those which are the target of Grünbaum’s objection, cannot be justified by the methods defended by Hopkins. (shrink)

Causal pluralism can be defended not only in respect to causal concepts and methodological guidelines, but also at the finer-grained level of causal inference from a particular source of evidence for causation. An argument for this last variety of pluralism is made based on an analysis of causal inference from randomized experiments (RCTs). Here, the causal interpretation of a statistically significant association can be established via multiple paths of reasoning, each relying on different assumptions and (...) providing distinct elements of information in favour of a causal interpretation. (shrink)

Abstract In his Aquinas Lecture 1992 at Marquette University, Ernan McMullin discusses whether there is a pattern of inference that particularly characterizes the sciences of nature. He pursues this theme both on a historical and a systematic level. There is a continuity of concern across the ages that separate the Greek inquiry into nature from our own vastly more complex scientific enterprise. But there is also discontinuity, the abandonment of earlier ideals as unworkable. The natural sciences involve many types (...) of inference; three of these interlock in a special way to produce “retroductive inference,” the kind of complex inference that supports causal theory. (shrink)

& Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses (...) some important probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. (shrink)

We argue that a modified version of Mill’s method of agreement can strongly confirm causal generalizations. This mode of causal inference implicates the explanatory virtues of mechanism, analogy, consilience, and simplicity, and we identify it as a species of Inference to the Best Explanation (IBE). Since rational causal inference provides normative guidance, IBE is not a heuristic for Bayesian rationality. We give it an objective Bayesian formalization, one that has no need of principles of indifference and yields (...) responses to the Voltaire objection, van Fraassen’s Bad Lot objection, and John Norton’s recent objection to IBE. (shrink)

This monograph looks at causal nets from a philosophical point of view. The author shows that one can build a general philosophical theory of causation on the basis of the causal nets framework that can be fruitfully used to shed new light on philosophical issues. Coverage includes both a theoretical as well as application-oriented approach to the subject. The author first counters David Hume’s challenge about whether causation is something ontologically real. The idea behind this is that good (...) metaphysical concepts should behave analogously to good theoretical concepts in scientific theories. In the process, the author offers support for the theory of causal nets as indeed being a correct theory of causation. Next, the book offers an application-oriented approach to the subject. The author shows that causal nets can investigate philosophical issues related to causation. He does this by means of two exemplary applications. The first consists of an evaluation of Jim Woodward’s interventionist theory of causation. The second offers a contribution to the new mechanist debate. Introductory chapters outline all the formal basics required. This helps make the book useful for those who are not familiar with causal nets, but interested in causation or in tools for the investigation of philosophical issues related to causation. (shrink)

Kerry et al. criticize our discussion of causal knowledge in evidence-based medicine (EBM) and our assessment of the relevance of their dispositionalist ontology for EBM. Three issues need to be addressed in response: (1) problems concerning transfer of causal knowledge across heterogeneous contexts; (2) how predictions about the effects of individual treatments based on population-level evidence from RCTs are fallible; and (3) the relevance of ontological theories like dispositionalism for EBM.

Error statistics is an important methodological view in philosophy of statistics and philosophy of science that can be applied to scientific experiments such as clinical trials. In this paper, I raise two potential issues for ES when it comes to guiding, and explaining early stopping of randomized controlled trials : ES provides limited guidance in cases of early unfavorable trends due to the possibility of trend reversal; ES is silent on how to prospectively control error rates in experiments requiring (...) multiple interim analyses. The method of conditional power, together with a rationing principle for RCTs, can assist ES in addressing such issues. (shrink)

In his Aquinas Lecture 1992 at Marquette University, Ernan McMullin discusses whether there is a pattern of inference that particularly characterizes the sciences of nature. He pursues this theme both on a historical and a systematic level. There is a continuity of concern across the ages that separate the Greek inquiry into nature from our own vastly more complex scientific enterprise. But there is also discontinuity, the abandonment of earlier ideals as unworkable. The natural sciences involve many types of (...) inference; three of these interlock in a special way to produce “retroductive inference,” the kind of complex inference that supports causal theory. (shrink)

Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of (...) the central concepts associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research. (shrink)

Despite their centrality to the scientific enterprise, both the nature of scientific variables and their relation to inductive inference remain obscure. I suggest that scientific variables should be viewed as equivalence classes of sets of physical states mapped to representations (often real numbers) in a structure preserving fashion, and argue that most scientific variables introduced to expand the degrees of freedom in terms of which we describe the world can be seen as products of an algorithmic (...) inductive inference first identified by William W. Rozeboom. This inference algorithm depends upon a notion of natural kind previously left unexplicated. By appealing to dynamical kinds—equivalence classes of causal system characterized by the interventions which commute with their time evolution—to fill this gap, we attain a complete algorithm. I demonstrate the efficacy of this algorithm in a series of experiments involving the percolation of water through granular soils that result in the induction of three novel variables. Finally, I argue that variables obtained through this sort of inductive inference are guaranteed to satisfy a variety of norms that in turn suit them for use in further scientific inferences. (shrink)

Frank Cabrera argues that informational explanatory virtues—specifically, mechanism, precision, and explanatory scope—cannot be confirmational virtues, since hypotheses that possess them must have a lower probability than less virtuous, entailed hypotheses. We argue against Cabrera’s characterization of confirmational virtue and for an alternative on which the informational virtues clearly are confirmational virtues. Our illustration of their confirmational virtuousness appeals to aspects of causal inference, suggesting that causal inference has a role for the explanatory virtues. We briefly explore this possibility, (...) delineating a path from Mill’s method of agreement to Inference to the Best Explanation (IBE). (shrink)

This paper naturalizes inductive inference by showing how scientific knowledge of real mechanisms provides large benefits to it. I show how knowledge about mechanisms contributes to generalization, inference to the best explanation, causal inference, and reasoning with probabilities. Generalization from some A are B to all A are B is more plausible when a mechanism connects A to B. Inference to the best explanation is strengthened when the explanations are mechanistic and when explanatory hypotheses are themselves mechanistically explained. (...) Causal inference in medical explanation, counterfactual reasoning, and analogy also benefit from mechanistic connections. Mechanisms also help with problems concerning the interpretation, availability, and computation of probabilities. (shrink)

Three competing ‘methods’ have been endorsed for inferring phylogenetic hypotheses: parsimony, likelihood, and Bayesianism. The latter two have been claimed superior because they take into account rates of sequence substitution. Can rates of substitution be justified on its own accord in inferences of explanatory hypotheses? Answering this question requires addressing four issues: (1) the aim of scientific inquiry, (2) the nature of why-questions, (3) explanatory hypotheses as answers to why-questions, and (4) acknowledging that neither parsimony, likelihood, nor Bayesianism are (...) inferential actions leading to explanatory hypotheses. The aim of scientific inquiry is to acquire causal understanding of effects. Observation statements of organismal characters lead to implicit or explicit why-questions. Those questions, conveyed in data matrices, assume the truth of observation statements, which is contrary to subsequently invoking substitution rates within inferences to phylogenetic hypotheses. Inferences of explanatory hypotheses are abductive in form, such that some version of an evolutionary theory(ies) is/are included or implied. If rates of sequence evolution are to be considered, it must be done prior to, rather than within abduction, which requires renaming those putatively-shared nucleotides subject to substitution rates. There are, however, no epistemic grounds for renaming characters to accommodate rates, calling into question the legitimacy of causally accounting for sequence data. (shrink)

Reactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and (...) distinguishes it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data. (shrink)

Gustav Theodor Fechner was one of the main proponents of inductive metaphysics in the 19th century. The idea of inductive metaphysics is to use empirical sources and inductive forms of inference in metaphysics. Although this sounds like a research program which might well appeal to scientifically minded philosophers, some of Fechner’s metaphysical conclusions look very suspicious from a scientific viewpoint. For example, Fechner famously argues that the planets and stars are animated by a soul and that the same holds (...) for the whole universe. In the present article it is shown that Fechner’s mystical cosmological views are based on a principle of analogy which is too permissive and that as a result of that his central argument to the conclusion that the earth is animated by a soul is not correct. As the article argues, the principle Fechner bases his inference on has to be supplemented by a causal condition along the lines of Mary Hesse’s account of analogical inferences, a condition Fechner at least implicitly recognizes but fails to meet. (shrink)

The framework of causal Bayes nets, currently influential in several scientific disciplines, provides a rich formalism to study the connection between causality and probability from an epistemological perspective. This article compares three assumptions in the literature that seem to constrain the connection between causality and probability in the style of Occam's razor. The trio includes two minimality assumptions—one formulated by Spirtes, Glymour, and Scheines (SGS) and the other due to Pearl—and the more well-known faithfulness or stability assumption. In (...) terms of logical strength, it is fairly obvious that the three form a sequence of increasingly stronger assumptions. The focus of this article, however, is to investigate the nature of their relative strength. The comparative analysis reveals an important sense in which Pearl's minimality assumption is as strong as the faithfulness assumption and identifies a useful condition under which it is as safe as SGS's relatively secure minimality assumption. Both findings have notable implications for the theory and practice of causal inference. 1 Introduction2 Background: Inference of Causal Structure in Markovian Causal Models3 Three Assumptions of Simplicity4 A Comparison of P-minimality and Faithfulness5 A Comparison of P-minimality and SGS-minimality6 Methodological Formulations and Prior Knowledge of Causal Order7 Conclusion. (shrink)

Causal knowledge unquestionably provides useful means to describe, explain and predict both natural and daily phenomena. This article addresses whether causality as such may not be ontologically primary and looks for an alternative fundamental mechanism encapsulating the information load of the causal framework. A probabilistic process view of reality asserting the struggle of natural forces is considered along with lines quoted from Nietzsche’s posthumously published notebooks and published work. Examples from scientific discoveries, in particular neurosciences, echoing his (...) ontology are provided. Furthermore, I propose a basic number game algorithm to illustrate and model the struggle of forces leading to causal inferences for an external observer. Nietzschean dynamic ontology involving the ceaseless struggle of will-to-power units accords with the emergentist approach to causation, which has been principally employed by the scientists working under the fields of thermodynamics, quantum mechanics and complex dynamical systems. (shrink)

There are several interpretations of the argument structure of Darwin's Origin of Species, representing Covering-Law, Inference-to-the-Best-Explanation, and (more recently) Semantic models. I argue that while all three types of interpretation enjoy some textual support, none succeeds in capturing the overall strategy of the Origin, consistent with Darwin's claim that it is 'one long argument'. I provide detailed criticisms of all three current models, and then offer an alternative interpretation based on the view that there are three main argument strategies in (...) the Origin, all supporting the 'causal efficacy' of Darwin's theory. This interpretation provides both a more unified treatment of the text, and some important implications concerning the relation between general philosophical models of scientific theory support and specific historical cases. (shrink)