The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained (...) by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations. (shrink)

An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the (...) empirical data. Serious engagement with cognitive science reveals that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed. (shrink)

There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell’s first statement of his notorious theorem in 1966. As is well known, its focus has mainly been the so-called Einstein-Podolsky-Rosen (“EPR”) thought experiment, and the ensuing observed correlations in real EPR like experiments. But although implicitly the discussion goes as far back as Bell’s work, it is only in the last two (...) decades that it has become recognizably and explicitly a debate about causal inference in the quantum realm. The bulk of this paper is devoted to a review of three influential arguments in the philosophical literature that aim to show that causal models for the EPR correlations are impossible, due to Bas Van Fraassen, Daniel Hausman and Huw Price. I contend that all these arguments are inconclusive since they contain premises or presuppositions that are false, unwarranted, or at least controversial. Five different common cause models are outlined that seem perfectly viable for the EPR correlations. These models are then employed to illustrate various difficulties with the premises and presuppositions underlying Van Fraassen’s, Hausman’s and Price’s arguments. In all these cases it is argued that the difficulties cut deep against these authors’ own theories of causation and causal inference. My conclusions are that causal models for the EPR correlations remain viable, that philosophical work is still required to assess their relative virtues, and that in any case the mere theoretical conceivability and empirical possibility of these models sheds doubts over Van Fraassen’s, Hausman’s and (important elements in) Price’s theories of causation and causal inference. (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)

Whenever the use of non-experimental data for discovering causal relations or predicting the outcomes of experiments or interventions is contemplated, two difficulties are routinely faced. One is the problem of latent variables, or confounders: factors influencing two or more measured variables may not themselves have been measured or recorded. The other is the problem of sample selection bias: values of the variables or features under study may themselves influence whether a unit is included in the data sample.

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)

The controversy over inference in perception turns on the nature of the processes that intervene between the stimulus and the perceptual experience or percept. Should the processes be viewed as something like inference and computation, or should they be viewed as psychologically primitive mechanisms whose workings are best accounted for at a neurological or physiological level? It is argued that the view that computational and inference-like processes play a role in perceptual processes should be adopted.

The present paper analyzes the regularities referred to via the concept 'self.' This is important, for cognitive science traditionally models the self as a cognitive mediator between perceptual inputs and behavioral outputs. This leads to the assertion that the self causes action. Recent findings in social psychology indicate this is not the case and, as a consequence, certain cognitive scientists model the self as being epiphenomenal. In contrast, the present paper proposes an alternative approach (i.e., the event-control approach) that is (...) based on recently discovered regularities between perception and action. Specifically, these regularities indicate that perception and action planning utilize common neural resources. This leads to a coupling of perception, planning, and action in which the first two constitute aspects of a single system (i.e., the distal-event system) that is able to pre-specify and detect distal events. This distal-event system is then coupled with action (i.e., effector-control systems) in a constraining, as opposed to 'causal' manner. This model has implications for how we conceptualize the manner in which one infers the intentions of another, anticipates the intentions of another, and possibly even experiences another. In conclusion, it is argued that it may be possible to map the concept 'self' onto the regularities referred to in the event-control model, not in order to reify 'the self' as a causal mechanism, but to demonstrate its status as a useful concept that refers to regularities that are part of the natural order. (shrink)

For nearly as long as the word “correlation” has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect them to probabilistic relations. One strand of this work studies the conditions under (...) which evidence accumulated from non-experimental (observational) studies can be used to infer a causal relationship. In this paper, I compare the typical conditions required to infer that one variable is a direct cause of another in observational and experimental studies. I argue that they are essentially the same. (shrink)

Causal inference plays a central role in behavioral science. Historically, behavioral science methodologies have typically sought to infer a single causal relation. Each of the major approaches to causal inference in the behavioral sciences follows this pattern. Nonetheless, such approaches sometimes differ in the causal relation that they infer. Incremental causal inference offers an alternative to this conceptualization of causal inference that divides the inference into a series of incremental (...) steps. Different steps infer different causal relations. Incremental causal inference is consistent with both causal pluralism and anti-pluralism. However, anti-pluralism places greater constraints the possible topology of sequential inferences. Arguments against causal inference include questioning consistency with causation as an explanatory principle, charging undue complexity, and questioning the need for it. Arguments in favor of incremental inference include better explanation of diverse causal inferences in behavioral science, tailored causal inference, and more detailed and explicit description of causal inference. Incremental causal inference offers a viable and potentially fruitful alternative to approaches limited to a single causal relation. (shrink)

The study of causal inference has seen recent momentum in machine learning and artificial intelligence, particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability. Yet, despite its increasing application to address many of the boundaries in modern AI, causal topics remain absent in most AI curricula. This work seeks to bridge this gap by providing classroom-ready introductions that integrate into traditional topics in AI, suggests intuitive graphical tools for the application to both new (...) and traditional lessons in probabilistic and causal reasoning, and presents avenues for instructors to impress the merit of climbing the “causal hierarchy” to address problems at the levels of associational, interventional, and counterfactual inference. Finally, this study shares anecdotal instructor experiences, successes, and challenges integrating these lessons at multiple levels of education. (shrink)

David Hume and other philosophers doubt that causality can be perceived directly. Instead, observers become aware of it through inference based on the perception of the two events constituting cause and effect of the causal relation. However, Hume and the other philosophers primarily consider causal relations in which one object triggers a motion or change in another. In this paper, I will argue against Hume’s assumption by distinguishing a kind of causal relations in which an (...) agent is controlling the motion or change of an object. I will call this kind of causal relations ‘causation-as-control’. In instances of causation-as-control, the observer does not become aware of the causality through inference based on the perception of two events (cause and effect). Rather, she perceives the two events directly and without further inference as cause and effect of a causal relation, and, therefore, the causality at work. (shrink)

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

The present paper analyzes the regularities referred to via the concept 'self.' This is important, for cognitive science traditionally models the self as a cognitive mediator between perceptual inputs and behavioral outputs. This leads to the assertion that the self causes action. Recent findings in social psychology indicate this is not the case and, as a consequence, certain cognitive scientists model the self as being epiphenomenal. In contrast, the present paper proposes an alternative approach (i.e., the event-control approach) that is (...) based on recently discovered regularities between perception and action. Specifically, these regularities indicate that perception and action planning utilize common neural resources. This leads to a coupling of perception, planning, and action in which the first two constitute aspects of a single system (i.e., the distal-event system) that is able to pre-specify and detect distal events. This distal-event system is then coupled with action (i.e., effector-control systems) in a constraining, as opposed to 'causal' manner. This model has implications for how we conceptualize the manner in which one infers the intentions of another, anticipates the intentions of another, and possibly even experiences another. In conclusion, it is argued that it may be possible to map the concept 'self' onto the regularities referred to in the event-control model, not in order to reify 'the self' as a causal mechanism, but to demonstrate its status as a useful concept that refers to regularities that are part of the natural order. (shrink)

Studying human beings as physicists have studied the world for so many years has been the aim of a substantial number of social scientists at least since the time of Comte. Basing their claim upon a certain metaphysics and anthropology, such thinkers have insisted over and over again that such a program is at least theoretically possible. But can a program, which cannot be put into practice on even a very rudimentary level, really be theoretically possible in the first place? (...) Physicists have known that space travel was theoretically possible ever since the seventeenth century and have made steady progress towards its implementation up until its recent fulfillment. Instead of making progress, however, the social scientists are still talking about what they are going to do some day when they acquire the tools for doing it. Even this would not be so bad, provided they knew exactly what kind of tools they needed and how to manufacture them. Today, though, even this pre-preliminary stage has not been brought to a successful conclusion. Some thinkers among the ranks of social scientists, however, are beginning to see their way clear to a better understanding of their collective predicament. H M Blalock Jr., professor of Sociology at the University of North Carolina, appears to be one of these thinkers. (shrink)

Throughout the Covid-19 pandemic, people have been eager to learn what factors, and especially what public health policies, cause infection rates to wax and wane. But figuring out conclusively what causes what is difficult in complex systems with nonlinear dynamics, such as pandemics. We review some of the challenges that scientists have faced in answering quantitative causal questions during the Covid-19 pandemic, and suggest that these challenges are a reason to augment the moral dimension of conversations about causal (...) inference. We take a lesson from Martha Nussbaum—who cautions us not to think we have just one question on our hands when we have at least two—and apply it to modeling for causal inference in the context of cost-benefit analysis. (shrink)

This book offers solutions to two persistent and I believe closely related problems in epistemology. The first problem is that of drawing a principled distinction between perception and inference: what is the difference between seeing that something is the case and merely believing it on the basis of what we do see? The second problem is that of specifying which beliefs are epistemologically basic (i.e., directly, or noninferentially, justified) and which are not. I argue that what makes a (...) belief a perceptual belief, or a basic belief, is not any introspectible feature of the belief but rather the nature of the cognitive system, or "module", that is causally responsible for the belief. Thus, even zombies, who in the philosophical literature lack conscious experiences altogether, can have basic, justified, perceptual beliefs. -/- The theories of perceptual and basic beliefs developed in the monograph are embedded in a larger reliabilist epistemology. I use this theory of basic beliefs to develop a detailed reliabilist theory: Inferentialist Reliabilism, which offers a reliabilist theory of inferential justification and which solves some longstanding problems for other reliabilist views by demanding inferential support for some—but not all—beliefs. The book is an instance of a thoroughgoingly naturalistic approach to epistemology. Many of my arguments have an empirical basis, and the view that I endorse reserves a central role for the cognitive sciences—in particular, cognitive neuroscience—in filling in the details of an applied epistemological theory. (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.

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)

Transcranial magnetic stimulation (TMS) is a method capable of transiently modulating neural excitability. Depending on the stimulation parameters information processing in the brain can be either enhanced or disrupted. This way the contribution of different brain areas involved in mental processes can be studied, allowing a functional decomposition of cognitive behavior both in the temporal and spatial domain, hence providing a functional resolution of brain/mind processes. The aim of the present paper is to argue that TMS with its ability to (...) draw causal inferences on function and its neural representations is a valuable neurophysiological tool for investigating the causal basis of neuronal functions and can provide substantive insight into the modern interdisciplinary and (anti)reductionist neurophilosophical debates concerning the relationships between brain functions and mental abilities. Thus, TMS can serve as a heuristic method for resolving causal issues in an arena where only correlative tools have traditionally been available. (shrink)

This paper examines Patrick Suppes’ probabilistic theory of causality understood as a theory of causal inference, and draws some lessons for empirical economics and contemporary debates in the foundations of econometrics. It argues that a standard method of empirical economics, multiple regression, is inadequate for most but the simplest applications, that the Bayes’ nets approach, which can be understood as a generalisation of Suppes’ theory, constitutes a considerable improvement but is still subject to important limitations, and that the (...) currently fashionable ‘design-based approach’ suffers from the same flaws Suppes anticipated a long time ago. It then sketches an alternative in response, one that differs drastically from the formalisms Suppes endorsed but is consistent with his pragmatic general take on science. (shrink)

In this paper I discuss an intriguing and relatively little studied symptomatic expression of schizophrenia known as experiences of activity in which patients form the delusion that they can control some external events by the sole means of their mind. I argue that experiences of activity result from patients being prone to aberrantly infer causal relations between unrelated events in a retrospective way owing to widespread predictive deficits. Moreover, I suggest that such deficits may, in addition, lead to an (...) aberrant intentional binding effect i.e., the subjective compression of the temporal interval between an intentional action and its external effects. In particular, it might be that patient’s thoughts are bound to the external events they aimed to control producing, arguably, a temporal contiguity between these two components. Such temporal contiguity would reinforce or sustain the feeling that the patient mind is directly causally efficient. (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.

Transcranial magnetic stimulation (TMS) is a method capable of transiently modulating neural excitability. Depending on the stimulation parameters information processing in the brain can be either enhanced or disrupted. This way the contribution of different brain areas involved in mental processes can be studied, allowing a functional decomposition of cognitive behavior both in the temporal and spatial domain, hence providing a functional resolution of brain/mind processes. The aim of the present paper is to argue that TMS with its ability to (...) draw causal inferences on function and its neural representations is a valuable neurophysiological tool for investigating the causal basis of neuronal functions and can provide substantive insight into the modern interdisciplinary and (anti)reductionist neurophilosophical debates concerning the relationships between brain functions and mental abilities. Thus, TMS can serve as a heuristic method for resolving causal issues in an arena where only correlative tools have traditionally been available. (shrink)

The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

"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)

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the (...) machine's activation that required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4-year-olds were found. We suggest that children's causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base rate of certain events occurring. Four-year-olds, but not 3-year-olds were able to make this inference. (shrink)

Ordinary language treats reports of perceptual episodes as canonical justifications of beliefs. The challenge for empirically oriented epistemologists is to explain one's right to give credence to one's perceptual judgments. Traditionally, many empiricists have assumed that an epistemic subject is entitled only to some primitive judgments, so that judgments about kinds, modal properties, and dispositions are parasitic upon and less certain than those about the particulars given in perception. This paper contributes to an understanding of C.S. Peirce's alternative perceptual (...) epistemology. According to Peirce's model, perceptual experience must be conceived as including previously encountered content and... (shrink)

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector”, a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the (...) machine’s activation that required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4- year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base-rate of certain events occurring. Fouryear-olds, but not 3-year-olds were able to make this inference. (shrink)

(2013). Cognitive shortcuts in causal inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 64-88. doi: 10.1080/19462166.2012.682655.

: Understanding Helmholtz's philosophy of science requires attention to his experimental practice. I sketch out such a project by showing how experiment shapes his theory of perception in three ways. One, the theory emerged out of empirical and experimental research. Two, the concept of experiment fills a critical conceptual gap in his theory of perception. Experiment functions not merely as a scientific technique, but also as a general epistemological strategy. Three, Helmholtz's experimental practice provides essential clues to the (...) interpretation of his theory of perception. A case study from experimental investigation of hearing shows how he designed such studies in accordance with the epistemological commitments of the theory of perception. Yet, while the theory was important to his experiments, the soundness of the experimental strategy was epistemically independent of those commitments. Secondly, the case study illustrates how Helmholtz consistently held that causal inferences underwrite reference to a real, but indirectly experienced external world. (shrink)

Recent research on “causal cognition” in adults and infants shows that we can perceive singular causal relations not previously experienced. In particular, infants that are able to perceive causality seem to rely on innate beliefs and principles that allow a priori inference of a connection between cause and effect. Can causal cognition in infants justify the thesis of causal realism? On the one hand, it weakens the central pillar of the Humean arguments: the impossibility of (...) a synthetic a priori causal inference. On the other hand, if perception is the privileged way of justifying the reality of objects of the external world, that is valid in the case of causal relations as well. Moreover, the perception of causal relations, based on innate principles and beliefs, reflects the selective results of the interaction between the real constraints of the physical structure of the world and the evolution of the human mind. (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)

Over the last two decades, a fundamental outline of a theory of causal inference has emerged. However, this theory does not consider the following problem. Sometimes two or more measured variables are deterministic functions of one another, not deliberately, but because of redundant measurements. In these cases, manipulation of an observed defined variable may actually be an ambiguous description of a manipulation of some underlying variables, although the manipulator does not know that this is the case. In this (...) article we revisit the question of precisely characterizing conditions and assumptions under which reliable inference about the effects of manipulations is possible, even when the possibility of “ambiguous manipulations” is allowed. (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)

In my paper, 'The science of psychoanalysis,' I make two assumptions. First, I assume that a 'hermeneutic science' is not a contradiction in terms. Second, I assume that explanations of why someone behaved as they did in terms of motives are a form of causal explanation, and therefore that inferring what someone's motives are from their behavior is a form of causal inference. In his commentary, Gipps objects to both of these assumptions, and this gives me the (...) opportunity to clarify them. Following the focus of Gipps's commentary, I shall concentrate primarily on the issue of science, and discuss causation only in relation to this.Herein, I am not concerned to defend a detailed positive theory of... (shrink)

This book seeks to offer original answers to all the major open questions in epistemology—as indicated by the book’s title. These questions and answers arise organically in the course of a validation of the entire corpus of human knowledge. The book explains how we know what we know, and how well we know it. The author presents a positive theory, motivated and directed at every step not by a need to reply to skeptics or subjectivists, but by the need of (...) a rational individual to know the world. -/- The author draws heavily from Ayn Rand’s theory of concepts as presented in her book, Introduction to Objectivist Epistemology, but departs from her epistemology—especially as explicated by leading exponents of her philosophy—in important ways. Areas of departure include the perception of entities and the role of probability theory in epistemology. -/- A main idea in the book is to begin the validation of the law of causality by identifying that existence causes our consciousness of it. Another key idea is this: The very fact that we perceive entities is the most basic and most extensive evidence that causal forces and causal interactions are regular. -/- Applying Ayn Rand’s principle that characteristics are ranges of measurement, the book offers a philosophical validation of “nonparametric predictive inference,” in turn providing an objective basis for prior probabilities in Bayesian analysis. -/- This book is primarily for a specialized readership—familiar with epistemological ideas and with Ayn Rand’s theory of concepts in particular. The last third of the book uses probability theory and mathematics somewhat beyond basic calculus, but less mathematical explanations are also included to make the general ideas accessible to lay readers. (shrink)

While there is virtually a consensus among contemporary philosophers of perception that some form of direct realism is true, there is less than complete agreement about whether normal, direct perceptions involve mental inferences in any sense. In taking another look at this recurrent question, my aim is twofold: first, to examine some of the arguments and evidences that have been offered in favor of inferences and to see if they can be accommodated within the direct realist framework, and second, (...) to attempt to clarify and defend the insight of direct realism that normal perceptions are noninferential. (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)