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)

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)

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.

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)

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)

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)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With (...) the help of an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)

Background: Although previous studies have shown an inverse relation between life meaning and eating disorder symptoms, the correlational nature of this evidence precludes causal inferences. Therefore, this study used an experimental approach to test the causal impact of life meaning on individuals' weight and shape concerns.Methods: Female students at risk for developing an eating disorder were randomly assigned to the control or the meaning condition, which involved thinking about and committing to pursue intrinsically valued life goals. A (...) color-naming interference task was used to assess the motivational salience of body-related stimuli, and self-report measures were used to assess participants' overvaluation of weight and shape.Results: The meaning manipulation was effective in activating intrinsically valued life goals. However, it did not result in lower self-reported overvaluation of weight and shape or lower color-naming interference effects of body-related stimuli, compared to the control condition. Post-hoc analyses suggested that baseline meaning in life was related to the impact of the manipulation.Conclusions: This experimental study did not provide evidence for a causal influence of life meaning on the overvaluation of weight and shape in a high-risk group. The current findings suggest that we first need to examine the relationship between life meaning and eating disorder symptoms in more detail, before implementing brief meaning manipulations in clinical practice. (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)

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)

Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. The text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

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)

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 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)

Excerpt: 1. Introduction This chapter provides insight into the diverse ethical debates on genetics and epigenetics. Much controversy surrounds debates about intervening into the germline genome of human embryos, with catchwords such as genome editing, designer baby, and CRISPR/Cas. The idea that it is possible to design a child according to one’s personal preferences is, however, a quite distorted view of what is actually possible with new gene technologies and gene therapies. These are much more limited than the editing and (...) design metaphors suggest. Such metaphors are therefore highly problematic phrases in the context of new gene technologies, for two reasons. On one hand, to design a child of choice by modifying the genome would require modifying any gene of choice, which is more than can be done with current gene technologies, such as CRISPR/Cas. On the other hand, a modification of genes would need to be enough to create any characteristic of choice in the future child. The latter presupposes the assumption of genetic determinism. Moreover, the CRISPR/Cas technology can not only be used in a potentially therapeutic manner at the germline level. In addition, there is the (more likely) scenario of a future clinical therapeutic use of these new gene technologies for modifying the DNA sequence of other cells of the body (somatic genome editing). There is also the option of modifying the epigenome, that is, the spatial configuration of DNA (epigenome editing) (see table 1). Like genetics and genome editing, epigenetics has been at the center of recent popular scientific and ethical discourse as well as scientific debates. The concept of epigenetics has given rise to very different notions of inheritability and responsibility for health, which, however, are oftentimes based on scientifically controversial basic assumptions. That there continues to be covert genetic determinism in the form of epigenetic determinism (see table 2) in debates about epigenetics has been pointed out in ethical analyses of epigenetics. Neither genetic determinism nor epigenetic determinism has been confirmed scientifically. It is therefore important to recognize the concepts that are discussed (and sometimes harshly criticized) in debates about genome editing and epigenetics—for example, concepts about the causal role of DNA for our own life course. This importance is based on the fact that if we understand such controversial concepts, we will be able to remain critical when evaluating scientific knowledge and ethical arguments about genome editing and epigenetics. This chapter, therefore, explains some of these concepts. For an ethical analysis of epigenetics as well as of genome editing, it is necessary to understand and critically reflect upon the underlying concepts of genetic determinism and other, related -isms. The following section offers a detailed introduction to these -isms (section 2; see also table 2). Section 3 provides an ethical analysis of genome editing and epigenetics based on the explanations in section 2. Section 3 focuses on inheritability and responsibility, justice, safety, the problem of consent, and the effects of genome editing and epigenetics on embryos and future generations. This section does not discuss in detail further points that can be found in ethical debates about epigenetics as well as in ethical debates about genome editing. These points include (among others): • fear that the findings of epigenetics and that the methods of genome editing are misused—this also with respect to eugenics and enhancement; • naturalness—an issue we mention in passing a few times in the following analysis; • a possible connection between the genome/epigenome and the concept of human dignity, and the derived danger of instrumentalization and infringement of autonomy when intervening in the genome or epigenome. Since current discourse about ethical issues associated with genome editing focuses mainly on germline interventions, which are, for instance, interventions into a human embryo’s genome, we mainly focus on germline interventions when comparing the debates on genome editing and on epigenetics in section 3. (shrink)

An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences Causal inference encompasses the tools that allow social scientists to determine what causes what. Economists--who generally can't run controlled experiments to test and validate their hypotheses--apply these tools to observational data to make connections. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied, whether the impact (or lack thereof) of increases in (...) the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the introduction of malaria nets in developing regions on economic growth. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and Stata programming languages. - -. (shrink)

"Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely (...) correlational data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data. (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)

This thesis aims to clarify a number of conceptual aspects of the debate surrounding algorithmic fairness. The particular focus here is the role of causal modeling in defining criteria of algorithmic fairness. In Chapter 1, I argue that in the discussion of algorithmic fairness, two fundamentally distinct notions of fairness have been conflated. Subsequently, I propose that what is usually taken to be the problem of algorithmic fairness should be divided into two subproblems, the problem of predictive fairness, and (...) the problem of allocative fairness. At the core of Chapter 2 is the proof of a theorem that establishes that three of the most popular (predictive) fairness criteria are pairwise incompatible. In particular, I show that under certain conditions, a predictive algorithm that satisfies a criterion called counterfactual fairness will with logical necessity violate two other popular predictive fairness criteria called equalized odds and predictive parity. In Chapter 3, a new predictive fairness criterion is developed using a mathematical framework for causal modeling. This fairness criterion, which I call causal relevance fairness, is a relaxation of another popular fairness criterion, counterfactual fairness, but turns out to be more closely in line with philosophical theories of discrimination. In Chapter 4, another infamous impossibility result in algorithmic fairness is analyzed through the lens of causality. I argue that by using a causal inference method called matching, we can modify the two fairness criteria equalized odds and predictive parity in a way that resolves the impossibility. Lastly, Chapter 5 contains an empirical case study. In it, the fairness of a popular recidivism risk prediction tool is analyzed using the criteria of (predictive) fairness developed earlier. (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)

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which (...) is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken. (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)

Yarkoni's argument risks skepticism about the very possibility of social science: If social phenomena are too causally complex, normal scientific methods could not possibly untangle them. We argue that the problem of causal complexity is best approached at the level of scientific communities and institutions, not the modeling practices of individual scientists.

Transcranial magnetic stimulation is used to make inferences about relationships between brain areas and their functions because, in contrast to neuroimaging tools, it modulates neuronal activity. The central aim of this article is to critically evaluate to what extent it is possible to draw causal inferences from repetitive TMS data. To that end, we describe the logical limitations of inferences based on rTMS experiments. The presented analysis suggests that rTMS alone does not provide the sort of premises that are (...) sufficient to warrant strong inferences about the direct causal properties of targeted brain structures. Overcoming these limitations demands a close look at the designs of rTMS studies, especially the methodological and theoretical conditions which are necessary for the functional decomposition of the relations between brain areas and cognitive functions. The main points of this article are that TMS-based inferences are limited in that stimulation-related causal effects are not equivalent to structure-related causal effects due to TMS side effects, the electric field distribution, and the sensitivity of neuroimaging and behavioral methods in detecting structure-related effects and disentangling them from confounds. Moreover, the postulated causal effects can be based on indirect effects. A few suggestions on how to manage some of these limitations are presented. We discuss the benefits of combining rTMS with neuroimaging in experimental reasoning and we address the restrictions and requirements of rTMS control conditions. The use of neuroimaging and control conditions allows stronger inferences to be gained, but the strength of the inferences that can be drawn depends on the individual experiment’s designs. Moreover, in some cases, TMS might not be an appropriate method of answering causality-related questions or the hypotheses have to account for the limitations of this technique. We hope this summary and formalization of the reasoning behind rTMS research can be of use not only for scientists and clinicians who intend to interpret rTMS results causally but also for philosophers interested in causal inferences based on brain stimulation research. (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)

At the close of his account of causality in the Treatise, Hume acknowledges that he had to adopt the “seemingly preposterous method” of examining the causal inference prior to analyzing the causal relation since the relation “depends so much on the inference”. This dependence emerges in his two definitions of ‘cause’ which, he concedes, seem “extraneous” to the causal relation. In this paper, I try to do what Hume did not do but could have done: fully describe (...) the causal relation so as to reach its core. That core is what I call “existential dependence,” the fact that one “object” exists because another exists. It turns out that Hume’s “preposterous method” is correct since existential dependence is what I call a “distributive” relation, a kind of conjunction that can be represented only by means of certain inferences. This makes existential dependence seem mysterious, but the mystery disappears if we interpret this dependence as probability, something that is comprehensible only through inferences. The key consequence of all of this is that we need to distinguish between the kind of causality that conforms to Hume’s two definitions of ‘cause’ and the kind of causality our causal inferences designate. (shrink)

According to neoclassical growth theory, there are two main patterns of economic growth, namely, intensive growth, which depends on total factor productivity, and extensive growth, which relies on factor input. This study explores the impacts of property taxes on growth patterns by considering the property tax pilots in Shanghai and Chongqing as a quasi-natural experiment. For evaluation, we applied multiple causal inference methods, including DID, PSM-DID, and a panel data approach for program evaluation. We found that the pilot of (...) Shanghai contributed to intensive growth, while the pilot of Chongqing reinforced the prevailing extensive growth. Specifically, Shanghai's property taxes restricted the buying of multiple homes and oversized homes, thereby reducing house prices and increasing TFP. Chongqing's property taxes are mainly for high-end houses, causing the substitution effect between high-end homes and ordinary houses; thus, the pilot increased the prices of ordinary houses and the average house price, which stimulated factor input and economic growth but decreased TFP. This study provides empirical evidence of the causal relationships between property taxes and growth patterns, indicating that transitional economies should avoid narrow tax bases during property tax reform for intensive growth. (shrink)

If one investigates a process that has several causes but assumes that it has only one cause, one risks ruling out important causal factors. Three mechanisms account for this mistake: either the significance of the single cause under test is masked by noise contributed by the unsuspected and uncontrolled factors, or the process appears only when two or more causes interact, or the process appears when there are present any of a number of sufficient causes which are not mutally (...) exclusive. In ecology and evolutionary biology, experiments usually test single factor hypotheses, and many scientists apparently believe that hypotheses incorporating several factors are so much more difficult to test that to do so would not be practical. We discuss several areas in ecology and evolutionary biology in which the presupposition of simple causation has apparently impeded progress. We also examine a more mature field, the study of atherosclerosis, in which single factor studies did significantly delay progress towards understanding what now appears to be a multifactor process. The problem has three solutions: either factorial experiments, dynamic models that make quantitative predictions, response-surface methods, or all three. In choosing a definition for ‘cause’, we make a presupposition that profoundly influences subsequent observations and experimental designs. Alternative definitions of causation should be considered as contributing to potential cures for research problems. (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)

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)

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)

S There is a long tradition of representing causal relationships by directed acyclic graphs (Wright, 1934 ). Spirtes ( 1994), Spirtes et al. ( 1993) and Pearl & Verma ( 1991) describe procedures for inferring the presence or absence of causal arrows in the graph even if there might be unobserved confounding variables, and/or an unknown time order, and that under weak conditions, for certain combinations of directed acyclic graphs and probability distributions, are asymptotically, in sample size, consistent. (...) These results are surprising since they seem to contradict the standard statistical wisdom that consistent estimators of causal effects do not exist for nonrandomised studies if there are potentially unobserved confounding variables. We resolve the apparent incompatibility of these views by closely examining the asymptotic properties of these causal inference procedures. We show that the asymptotically consistent procedures are ‘pointwise consistent’, but ‘uniformly consistent’ tests do not exist. Thus, no finite sample size can ever be guaranteed to approximate the asymptotic results. We also show the nonexistence of valid, consistent confidence intervals for causal effects and the nonexistence of uniformly consistent point estimators. Our results make no assumption about the form of the tests or estimators. In particular, the tests could be classical independence tests, they could be Bayes tests or they could be tests based on scoring methods such as  or . The implications of our results for observational studies are controversial and are discussed briefly in the last section of the paper. The results hinge on the following fact: it is possible to find, for each sample size n, distributions P and Q such that P and Q are empirically indistinguishable and yet P and Q correspond to different causal effects. (shrink)

Isaac Newton's Scientific Method examines Newton's argument for universal gravity and his application of it to resolve the problem of deciding between geocentric and heliocentric world systems by measuring masses of the sun and planets. William L. Harper suggests that Newton's inferences from phenomena realize an ideal of empirical success that is richer than prediction. Any theory that can achieve this rich sort of empirical success must not only be able to predict the phenomena it purports to explain, but (...) also have those phenomena accurately measure the parameters which explain them. Harper explores the ways in which Newton's method aims to turn theoretical questions into ones which can be answered empirically by measurement from phenomena, and to establish that propositions inferred from phenomena are provisionally accepted as guides to further research. This methodology, guided by its rich ideal of empirical success, supports a conception of scientific progress that does not require construing it as progress toward Laplace's ideal limit of a final theory of everything, and is not threatened by the classic argument against convergent realism. Newton's method endorses the radical theoretical transformation from his theory to Einstein's. Harper argues that it is strikingly realized in the development and application of testing frameworks for relativistic theories of gravity, and very much at work in cosmology today. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Journal of the History of Philosophy 39.3 (2001) 351-383 [Access article in PDF] Hume's Pyrrhonian Skepticism and the Belief in Causal Laws Graciela De Pierris Hume endorses in no uncertain terms the normative use of causal reasoning. The most striking example of this commitment is Hume's argument in the Enquiry against the possibility of miracles. The argument sanctions, in particular, the use of scientific reflection on (...) uniform experience issuing in causal laws of nature (E 114): "A miracle is a violation of the laws of nature; and as a firm and unalterable experience has established these laws, the proof against a miracle, from the very nature of the fact, is as entire as any argument from experience can possibly be imagined." 1 These words should give us pause; for how can Hume so confidently say, after his skeptical conclusions concerning our causal beliefs, that "a firm and unalterable experience has established" the laws of nature, and that we can therefore produce "proofs" against miracles? The Treatise gives rise to the same puzzle. In this text, too, Hume confidently prescribes methodical reflection based on causal reasoning. For example, in Book One, Part Three, Section Fifteen, Hume offers eight "Rules by which to judge of causes and effects." These rules are more than descriptions of our natural causal inferences. They are meant to guide us in making better causal inferences; in Hume's words they "ought to regulate" our probable reasoning since (T 149): "by them we learn to distinguish the accidental circumstances from the efficacious causes." 2Moreover, Hume consistently takes Newtonian science as a model for the proper employment of causal inferences in inquiries into matters of fact and, [End Page 351] in particular, as a model to be followed in his own science of human nature. 3 This paradigm of what the science of nature ought to be is understood by Hume as an inductive, experimental inquiry. 4 In the Enquiry, for example, Hume uses the label "sciences" for "politics, natural philosophy, physic, chemistry, &c." and in the preceding discussion argues that these sciences are not a priori but are based on causal reasoning (E 164-165). Thus, there are for Hume inquiries into matters of fact and existence that amount to more than blindly following natural propensities and habit. The sciences and many of our causal inferences in common life involve more than mere belief as a sentiment. They involve, in addition, careful reflection that results in improved causal inferences and thus provides normative standards. Yet, in thus upholding the normative use of causal reasoning in science and in everyday life, Hume appears to contradict his own radical skeptical conclusions concerning our knowledge of causation and induction. I here propose a novel way to resolve this puzzle.To acknowledge the existence of a puzzle, however, amounts to accepting the now controversial view that Hume provides, and takes at face value, a radical skeptical argument concerning our knowledge of causation and induction. 5 Moreover, the attempt to resolve this puzzle involves rejecting the suggestion [End Page 352] that Hume, without awareness or concern, happily holds flatly inconsistent views. 6 It also goes against the more sophisticated suggestion that Hume lightly alternates among different psychological moods, or perhaps different perspectives, which might eventually issue in results that contradict each other but are sufficiently localized so as to prevent serious conflict. Such a view might lead us to conceive Hume's texts as an unstructured and arbitrary patchwork of reflections, driven by different psychological moods or by the embellishments of a radiant literary style. This kind of reading is unacceptable as an interpretative tool, however, as H. J. Paton has acutely noted in his criticism of the patchwork interpretation of Kant's Critique of Pure Reason. 7 For such a reading would place no constraints at all on the task of establishing which parts of Hume's text belong to which moods or perspectives. The suggestion that Hume offers an unstructured patchwork of reflections masks the novel and systematic distinction Hume draws between his radical philosophical... (shrink)

Numerous scholarly works focus solely on scientific metaphysics or biological practice, but few attempt to bridge the two subjects. This volume, the latest in the Minnesota Studies in the Philosophy of Science series, explores what a scientific metaphysics grounded in biological practices could look like and how it might impact the way we investigate the world around us. From Biological Practice to Scientific Metaphysics examines how to reconcile the methods of biological practice with the methods of metaphysical (...) cosmology, notably regarding the origins of life. The contributors take up a wide range of traditional metaphysics and philosophy of science topics, including natural kinds, medicine, ecology, genetics, scientific pluralism, reductionism, operationalism, mechanisms, the nature of information, and more. Many of the chapters represent the first philosophical treatments of significant biological practices. From causality and complexity to niche constructions and inference, the contributors review and discuss long-held objections to metaphysics by natural scientists. They illuminate how, in order to learn about the world as it truly is, we must look not only at what scientists say but also what they do: for ontology cannot be read directly from scientific claims. (shrink)

One conception of underdetermination is that it corresponds to the impossibility of reliable inquiry. In other words, underdetermination is defined to be the situation where, given a set of background assumptions and a space of hypotheses, it is logically impossible for any hypothesis selection method to meet a given reliability standard. From this perspective, underdetermination in a given subject of inquiry is a matter of interplay between background assumptions and reliability or success criteria. In this paper I discuss underdetermination in (...) causal inference along this line. In particular I will analyze several success criteria that can be applied to causal inference from statistical regularities. The criteria center on the notions of consistency in mathematical statistics. For each criterion I present its epistemic implication in terms of simple conditions under which the criterion cannot possibly be met. I then investigate which of the familiar principles and their variants in the literature, if adopted as background assumptions, are sufficient to overcome different levels of underdetermination induced by the success criteria. “亚决定性”是知识论和科学哲学中一个重要的概念。对这个概念的一种阐释是把它对应于可靠探索的不可能性。就是说，在一个（经验）问题中，给定一些公设和一些供选择的理论或假说，如果逻辑上不可能找到一种理论选择 的方法能满足一定的可靠或成功标准，那么相对于这个标准就存在亚决定性。从这个观点看，亚决定性总是相对于一个问题设定，尤其是公设和成功标准而言的。本文从这个角度对近来的统计因果推理研究作一番梳理。首先，基 于数理统计中的一致性概念，我会讨论和分析一系列可应用于因果推理的成功标准。对每一个标准，我会用一个相对简单的条件来刻画它对应的亚决定性。然后我对文献里一部分重要的结果作一个综述，以澄清什么样的公设可以 消除什么样的亚决定性。. (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)

A central issue confronting both philosophers and practitioners in formulating an analysis of causation is the question of what constitutes evidence for a causal association. From the 1950s onward, the biostatistician Jerome Cornfield put himself at the center of a controversial debate over whether cigarette smoking was a causative factor in the incidence of lung cancer. Despite criticisms from distinguished statisticians such as Fisher, Berkson and Neyman, Cornfield argued that a review of the scientific evidence supported the conclusion (...) of a causal association. Cornfield's odds ratio in case‐control studies — as a good estimate of relative risk — together with his argument of ''explanatory common cause'' became important tools to use in confronting the skeptics. In this paper, I revisit this important historical episode as recorded in the Journal of National Cancer Institute and the Journal of the American Statistical Association. More specifically, I examine Cornfield's necessary condition on the minimum magnitudes of relative risk in light of confounders. This episode yields important insight into the nature of causal inference by showing the sorts of evidence appealed to by practitioners in supporting claims of causal association. I discuss this event in light of the manipulationist account of causation. (shrink)

Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a (...) grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions. (shrink)

This book examines how legal causation inference and epidemiological causal inference can be harmonized within the realm of jurisprudence, exploring why legal causation and epidemiological causation differ from each other and defining related problems. The book also discusses how legal justice can be realized and how victims’ rights can be protected. It looks at epidemiological evidence pertaining to causal relationships in cases such as smoking and the development of lung cancer, and enables readers to correctly interpret and rationally (...) use the results of epidemiological studies in lawsuits. The book argues that in today’s risk society, it is no longer possible to thwart the competence of evidence using epidemiological research results. In particular, it points out that the number of cases that struggle to prove a causal relationship excluding those using epidemiological data will lead to an increase in the number of lawsuits for damages that arise as a result of harmful materials that affect our health. The book argues that the responsibility to compensate for damages that have actually occurred must be imputed to a particular party and that this can be achieved by understanding causal inferences between jurisprudence and epidemiology. This book serves as a foundation for students, academics and researchers who have an interest in epidemiology and the law, and those who are keen to discover how jurisprudence can bring these two areas together. (shrink)

Graphical models are a powerful tool for causal model specification. Besides allowing for a hierarchical representation of variable interactions, they do not require any a priori specification of the functional dependence between variables. The construction of such graphs hence often relies on the mere testing of whether or not model variables are marginally or conditionally independent. The identification of causal relationships then solely requires some general assumptions on the relation between stochastic and causal independence, such as the (...) Causal Markov Condition and the Faithfulness Condition (Spirtes et al. 2000; Pearl 2000). However, a procedure would require further assumptions to hold. Namely those the independence tests themselves are based on. In continuous settings, Spirtes et al. (2000) suggest causal inference based on a very restrictive formulation of independence, that is, vanishing partial correlations. Such a measure does, however, limit the applicability of causal inference to linear systems. This constitutes a serious drawback especially for the social sciences where an a priori specification of the functional form proves difficult or at odds with linearity. In short: graphical models theoretically reduce specification uncertainty regarding functional dependence, but their implementation in practice deprives them of this virtue. In this paper we investigate how causal structures in continuous settings can be identified when both functional forms and probability distributions of the variables remain unspecified. We focus on tests exploiting the fact that if X and Y are conditionally independent given a set of variables Z, the two conditional densities f (X|Y, Z) and f (X|Z) must coincide. We start by estimating the conditional densities f(X|Y, Z) and f(X|Z) via nonparametric techniques (kernel methods). We proceed by testing if some metric expressing the distance between these very conditional densities is sufficiently close to zero. Out of several metrics available in the literature to express such distance we choose two, the Euclidean, and the Hellinger distance. We investigate in a Monte Carlo study how different tests involving either measure are able to detect statistical independence, conditioned on a small set of variables. One limitation may result from nonparametric density estimation being subject to the curse of dimensionality. As the number of variables increases, the estimated empirical density converges at a slower rate to its population value. To compensate this drawback we use a local bootstrap procedure which consists of resampling the data for each test. While local bootstrap strongly increases the computational time of the test, it succeeds in counterbalancing the curse of dimensionality. (shrink)

Approximately one in six top economic research papers draws an explicitly causal conclusion. But what do economists mean when they conclude that A 'causes' B? Does 'cause' say that we can influence B by intervening on A, or is it only a label for the correlation of variables? Do quantitative analyses of observational data followed by such causal inferences constitute sufficient grounds for guiding economic policymaking? The Philosophy of Causality in Economics addresses these questions by analyzing the meaning (...) of causal claims made by economists and the philosophical presuppositions underlying the research methods used. The book considers five key causal approaches: the regularity approach, probabilistic theories, counterfactual theories, mechanisms, and interventions and manipulability. Each chapter opens with a summary of literature on the relevant approach and discusses its reception amongst economists. The text details case studies, and goes on to examine papers which have adopted the approach in order to highlight the methods of causal inference used in contemporary economics. It analyses the meaning of the causal claim put forward, and finally reconstructs the philosophical presuppositions accepted implicitly by economists. The strengths and limitations of each method of causal inference are also considered in the context of using the results as evidence for policymaking. This book is essential reading to those interested in literature on the philosophy of economics, as well as the philosophy of causality and economic methodology in general. (shrink)

The protection of European consumers from the false or misleading scientific and nutritional claims of food manufacturers took a step forward with the recent opinions of the European Food Safety Authority (EFSA). As a risk assessment agency, the EFSA recently assessed and rejected a vast number of food claim forcing the withdrawal of many claims from leading manufacturers. Focusing on the functional food sector, consumer protection issues, and market impacts, this article looks into the role of the EFSA (...) and examines the rejection of claims based on bad and misleading science and outlines how the EFSA map claims and distinguishes between the good and bad scientific methods. (shrink)