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

One challenge that has to be addressed by the fast and frugal heuristics program is how people manage to select, from the abundance of cues that exist in the environment, those to rely on when making decisions. We hypothesize that causal knowledge helps people target particular cues and estimate their validities. This hypothesis was tested in three experiments. Results show that when causal information about some cues was available, participants preferred to search for these cues first and (...) to base their decisions on them. When allowed to learn cue validities in addition to causal information, participants also became more frugal, made more accurate decisions, and were more precise in estimating cue validities than was a control group that did not receive causal information. These results can be attributed to the causal relation between the cues and the criterion, rather than to greater saliency of the causal cues. Overall, our results support the hypothesis that causal knowledge aids in the learning of cue validities and is treated as a meta-cue for identifying highly valid cues. (shrink)

Cognitive Science, Volume 46, Issue 5, May 2022.

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

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in (...) the environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities. (shrink)

What are the roles of scientific causal knowledge in relation to the evidential requirements of policy making? In this chapter, I review the existing approaches in philosophy of science to the policy relevance of causal knowledge. I assess the specific concerns and questions on which these philosophical accounts have focused and show how they only offer a partial perspective of the relation between causal knowledge and policy making. Most existing views are top-down approaches: they (...) start from philosophical concerns about causation and evidence, and then make general claims about (potential) policy implications, but leave the notion of “policy” unanalyzed. As an alternative, I put forward some ideas toward a bottom-up approach: one that understands policy making as a dynamic process with several stages, and that starts from concrete policy problems, to then examine how causal notions and methods can contribute in various ways to the specifics of the process of policy making. (shrink)

I argue that scientific realism, insofar as it is only committed to those scientific posits of which we have causal knowledge, is immune to Kyle Stanford’s argument from unconceived alternatives. This causal strategy is shown not to repeat the shortcomings of previous realist responses to Stanford’s argument. Furthermore, I show that the notion of causal knowledge underlying it can be made sufficiently precise by means of conceptual tools recently introduced into the debate on scientific realism. (...) Finally, I apply this strategy to the case of Jean Perrin’s experimental work on the atomic hypothesis, disputing Stanford’s claim that the problem of unconceived alternatives invalidates a realist interpretation of this historical episode. 1 Stanford’s Argument from Unconceived Alternatives2 Previous Attempts to Undermine the Problem of Unconceived Alternatives2.1 The plausibility of unconceived alternatives2.2 The distinctness of unconceived alternatives2.3 The induction from past to present3 Causal Knowledge as a Criterion for the Realist3.1 How Chakravartty’s proposal differs from earlier causal strategies3.2 Causal realism and the detection/auxiliary distinction4 Causal Realism, Unconceived Alternatives, and the Atomic Hypothesis4.1 Perrin and the philosophers: some initial observations4.2 Roush and Stanford on Perrin4.3 From Brownian motion to the reality of atoms4.4 What we know about atoms5 Conclusion. (shrink)

In this article I consider the challenges for exporting causal knowledge raised by complex biological systems. In particular, James Woodward’s interventionist approach to causality identified three types of stability in causal explanation: invariance, modularity, and insensitivity. I consider an example of robust degeneracy in genetic regulatory networks and knockout experimental practice to pose methodological and conceptual questions for our understanding of causal explanation in biology. †To contact the author, please write to: Department of History and Philosophy (...) of Science, University of Pittsburgh, 1017 Cathedral of Learning, Pittsburgh, PA 15260; e‐mail: [email protected]. (shrink)

Experiences of having caused a certain outcome may arise from motor predictions based on action–outcome probabilities and causal inferences based on pre-activated outcome representations. However, when and how both indicators combine to affect such self-agency experiences is still unclear. Based on previous research on prediction and inference effects on self-agency, we propose that their contribution crucially depends on whether people have knowledge about the causal relation between actions and outcomes that is relevant to subsequent self-agency experiences. Therefore, (...) we manipulated causal knowledge that was either relevant or irrelevant by varying the probability of co-occurrence of specific actions and outcomes. Afterwards, we measured self-agency experiences in an action–outcome task where outcomes were primed or not. Results showed that motor prediction only affected self-agency when relevant actions and outcomes were learned to be causally related. Interestingly, however, inference effects also occurred when no relevant causal knowledge was acquired. (shrink)

Behavior genetics often emphasizes methods over the underlying quality of the psychological information to which the methods are applied. A core aspect of this quality is the demographic diversity of the samples. Building causal genetic models based only on European-ancestry samples compromises their generalizability. Behavior genetics researchers must spend additional time and resources diversifying their samples before emphasizing causation.

James M. Robins, Richard Scheines, Peter Spirtes, and Larry Wasserman. The Limits of Causal Knowledge.

Theories of how organisms learn about cause-effect relations have a history dating back at least to the associationist/mechanistic hypothesis of David Hume. Some contemporary theories of causal learning are descendants of Hume's mechanistic models of conditioning, but others impute principled, rule-based reasoning. Since even primitive animals are conditionable, it is clear that there are built-in mechanical algorithms that respond to cause/effect relations. The evidence suggests that humans retain the use of such algorithms, which are surely adaptive when causal (...) judgments must be rapidly made. But we know very little about what these algorithms are and about when and with what ratiocinative procedures they are sometimes replaced. Nor do we know how the concept of causation originates in humans. To clarify some of these issues, this paper surveys the literature and explores the behavioral predictions made by two contrasting theories of causal learning: the mechanical Rescorla-Wagner model and the sophisticated reasoning codified in Bayes' Theorem. (shrink)

Concrete Causation centers about theories of causation, their interpretation, and their embedding in metaphysical-ontological questions, as well as the application of such theories in the context of science and decision theory. The dissertation is divided into four chapters, that firstly undertake the historical-systematic localization of central problems (chapter 1) to then give a rendition of the concepts and the formalisms underlying David Lewis' and Judea Pearl's theories (chapter 2). After philosophically motivated conceptual deliberations Pearl's mathematical-technical framework is drawn on for (...) an epistemic interpretation and for emphasizing the knowledge-organizing aspect of causality in an extension of the interventionist Bayes net account of causation (chapter 3). Integrating causal and non-causal knowledge in unified structures ultimately leads to an approach towards solving problems of (causal) decision theory and at the same time facilitates the representation of logical-mathematical, synonymical, as well as reductive relationships in efficiently structured, operational nets of belief propagation (chapter 4). (shrink)

Three experiments investigated reasoners' beliefs about causal powers; that is, their beliefs about the capacity of a putative cause to produce a given effect. Covariation-based theories (e.g., Cheng, 1997; Kelley, 1973; Novick & Cheng, 2004) posit that beliefs in causal power are represented in terms of the degree of covariation between the cause and its effect; covariation is defined in terms of the degree to which the effect occurs in the presence of the cause, and fails tooccur in (...) the absence of the cause. To test the degree to which beliefs incausal power are reflected in beliefs about covariation information, participants in three experiments rated their beliefs that putative causes have the capacity to produce a given effect (i.e., possess the causal power to produce an effect) as well as their beliefs regarding the degree to which the putative cause and effectcovary. A strong positive correlation was discovered between participants' beliefs in causal power and their beliefs that the effect occurs in the presence of the cause. However, no direct relationship was found between participants' beliefs in causal power and their belief that the effect will fail tooccur in the absence of the cause. These findings were replicated using bothwithin- (Experiments 1 and 3) and between-subject designs (Experiment 2). In Experiment 3, we extended these analyses to measures of familiarity, imageability, and detailedness of the representation. We found that participants' beliefs in causal power were strongly associated with familiarity, and imageability, but not the perceived detailedness of the cause and effect relationship. These data provide support for a multidimensional account of causal knowledge whereby people's representations of causation include, but are not limited to, the covariation, familiarity, and imageability of cause and effect relationships. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) class='Hi'>knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

Temporal logic of causal knowledge over general partially ordered structures of local states is defined. The definition of knowledge captures the change of state due to action executions. The structures are a variant of flow event structures including prime event structures and branching processes of Petri Nets. Modalities corresponding to the causality, concurrency, and indistinguishability relations are used. Formulas are interpreted over local state occurrences. The logic is proved to be decidable and a complete axiomatization is provided.

In Nozick’s rendition of the decision situation given in Newcomb’s Paradox dominance and the principle of maximum expected utility recommend different strategies. While evidential decision theory seems to be split over which principle to apply and how to interpret the principles in the first place, causal decision theory seems to go for the solution recommended by dominance. As a reply to the CDT proposal by Wolfgang Spohn, who opts for “one-boxing” by employing reflexive decision graphs, I will draw on (...) the framework of causal knowledge patterns, i.e., Bayes net causal models, augmented by non-causal knowledge, to finally arrive at “one-boxing” – more intuitively and more closely to what actually is in Nozick’s story. This proposal allows the careful re-examination of all relevant concepts in the original story and might cast new light on the following questions: How may causality in general be understood to allow causal inference from hybrid patterns encoding subjective knowledge? How can the notion of prediction be analyzed – philosophically and formally? And what’s the decision-maker’s conceptualization of the situation he will act upon? (shrink)

These essays critically reassess the widely accepted view that statistical methods of analysis can, and do, yield causal understanding of social phenomena. They emphasize the historical, philosophical and conceptual perspectives that underlie and inform current methodological controversies.

Leading philosophical accounts presume that Thomas H. Morgan’s transmission theory can be understood independently of experimental practices. Experimentation is taken to be relevant to confirming, rather than interpreting, the transmission theory. But the construction of Morgan’s theory went hand in hand with the reconstruction of the chief experimental object, the model organism Drosophila melanogaster . This raises an important question: when a theory is constructed to account for phenomena in carefully controlled laboratory settings, what knowledge, if any, indicates the (...) theory’s relevance to phenomena outside highly controlled settings? The answer, I argue, is found within the procedural knowledge embedded within laboratory practice. †To contact the author, please write to: Minnesota Center for Philosophy of Science, University of Minnesota, Department of Philosophy, 831 Heller Hall, 271 19th Ave., University of Minnesota, Minneapolis, MN 55455‐0310; e‐mail: [email protected]. (shrink)

We are less optimistic than Madole & Harden that family-based genome-wide association studies (GWASs) will lead to significant second-generation causal knowledge. Despite bearing some similarities, family-based GWASs and randomised controlled trials (RCTs) are not identical. Most RCTs assess a relatively homogenous causal stimulus as a treatment, whereas GWASs assess highly heterogeneous causal stimuli. Thus, GWAS results will not translate so easily into second-generation causal knowledge.

How do humans discover causal relations when the effect is not immediately observable? Previous experiments have uniformly demonstrated detrimental effects of outcome delays on causal induction. These findings seem to conflict with everyday causal cognition, where humans can apparently identify long-term causal relations with relative ease. Three experiments investigated whether the influence of delay on adult human causal judgements is mediated by experimentally induced assumptions about the timeframe of the causal relation in question, as (...) suggested by Einhorn and Hogarth (1986). Causal judgements generally decreased when a delay separated cause and effect. This decrease was less pronounced when the thematic context of the causal relation induced participants to expect a delay. Experiment 3 ruled out an alternative explanation of the effect based on variations of cue and outcome saliencies, and showed that detrimental effects of delay are reduced even more when instructions explicitly mentioned the timeframe of the causal relation in question. Knowledge thus mediates the impact of delay on human causal judgement. Implications for contemporary theories of human causal induction are discussed. (shrink)

We study abductive, causal, and non-causal conditionals in indicative and counterfactual formulations using probabilistic truth table tasks under incomplete probabilistic knowledge (N = 80). We frame the task as a probability-logical inference problem. The most frequently observed response type across all conditions was a class of conditional event interpretations of conditionals; it was followed by conjunction interpretations. An interesting minority of participants neglected some of the relevant imprecision involved in the premises when inferring lower or upper probability (...) bounds on the target conditional/counterfactual ("halfway responses"). We discuss the results in the light of coherence-based probability logic and the new paradigm psychology of reasoning. (shrink)

Book Information Knowledge, Cause, and Abstract Objects: Causal Objections to Platonism. Knowledge, Cause, and Abstract Objects: Causal Objections to Platonism Colin Cheyne , Dordrecht: Kluwer Academic Publishers , 2001 , xvi + 236 , £55 ( cloth ) By Colin Cheyne. Dordrecht: Kluwer Academic Publishers. Pp. xvi + 236. £55.

When we try to think about the causal nexus and the physical nature of the world as a whole we may be struck by two quite different difficulties in finding room in it to accommodate together knowledge or reasoned belief and causal determinism. may seem to us to exclude and may seem to us to exclude . Taking it as a fact that there is knowledge and that knowledge seems to be indefinitely extensible, it has (...) been felt by some philosophers that we can disprove total determinism by showing that if there were laws of nature which purported to govern all movements of matter in the universe there would still be something which even an ‘all-knowing’ predicter could not predict, viz. his own predictions or his own actions; and that given enough knowledge any agent could refute anybody else's predictions of his actions. So it has been thought that the phenomenon of knowledge somehow shows there cannot be laws to govern all movements of matter in the universe. This comfortably anodyne reflection is examined in the second part of the lecture. It elevates human minds and even confers a sort of cosmic importance on them. The other difficulty in making room for both and is in some loose sense the dual of this. Instead of taking knowledge for granted and questioning total determinism, it merely takes causality for granted but then deduces the total impossibility of knowledge. It simply asks: ‘How can we take a belief seriously, or consider it seriously as a candidate to be knowledge, if it is no better than a simple physical effect?’ This is a more pessimistic reflection and I shall begin with it. (shrink)

One of the principal presuppositions in the extended mind account of Clark and Chalmers establishes that extended and non-extended cognitive systems have somehow the same structure and that the distinctions between them can only be superficial. In contrast, this work presents some arguments for the idea that it is possible to find fundamental differences between both, mainly on the basis that a criterion that does not include the notion of knowledge is not strong enough to define cognitive processes. A (...) brief analysis on the non-transitivity of trust and the notion of causal dependence between information and cognitive systems might be helpful to support this position. It will be argued that the counterfactual block which supports the extended mind building does not seem to be firm. (shrink)

According to platonists, entities such as numbers, sets, propositions and properties are abstract objects. But abstract objects lack causal powers and a location in space and time, so how could we ever come to know of the existence of such impotent and remote objects? In Knowledge, Cause, and Abstract Objects, Colin Cheyne presents the first systematic and detailed account of this epistemological objection to the platonist doctrine that abstract objects exist and can be known. Since mathematics has such (...) a central role in the acquisition of scientific knowledge, he concentrates on mathematical platonism. He also concentrates on our knowledge of what exists, and argues for a causal constraint on such existential knowledge. Finally, he exposes the weaknesses of recent attempts by platonists to account for our supposed platonic knowledge. This book will be of particular interest to researchers and advanced students of epistemology and of the philosophy of mathematics and science. It will also be of interest to all philosophers with a general interest in metaphysics and ontology. (shrink)

Goldman (1967) proposed that a subject s knows p if and only if p is appropriately causally connected to s’s believing p. He later on abandoned this theory (Goldman, 1976). The main objection to the theory is that the causal connection required by Goldman is compatible with certain problematic forms of luck. In this paper we argue that Goldman’s causal theory of knowledge can overcome the luck problem if causation is understood along interventionist lines. We also show (...) that the modified theory leads to the correct results in contexts involving other prominent forms of epistemic luck and compare it with other accounts on the market. (shrink)

Causal pluralism is currently a hot topic in philosophy. However, the consequences of this view on causation for scientific knowledge and scientific methodology are heavily underexposed in the present debate. My aim in this paper is to argue that an epistemological-methodological point of view should be valued as a line of approach on its own and to demonstrate how epistemological- methodological causal pluralism differs in its scope from conceptual and metaphysical causal pluralism. Further, I defend epistemological-methodological (...) causal pluralism and try to illustrate that scientific practice needs diverse causal concepts in diverse domains, and even diverse causal concepts within singular domains. (shrink)

In his 1967 paper 'A Causal Theory of Knowing', Alvin Goldman sketched an account of empirical knowledge in terms of appropriate causal connections between the fact known and the knower's belief in that fact. This early causal account has been much criticized, even by Goldman himself in later years. We argue that the theory is much more defensible than either he or its other critics have recognized, that there are plausible internal and external resources available to (...) it which save it from many objections in the literature (in particular, objections raised by Harman, Pappas and Swain, Klein, Dretske, Goldman, Shope, Ackermann, Morawetz, and Collier). (shrink)

Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, problems arise for using safety as a condition on knowledge: safety is not necessary for knowledge and cannot always explain the Gettier cases and cases of statistical evidence it is meant to address. In this paper, I argue for a new modal condition designed to capture the non-accidental relationship between facts and evidence (...) required for knowledge: causal safety. I argue that possible errors in belief can be captured by accounting for deviations in causal relationships and that there is a natural way to characterize which causal errors are relevant in an epistemic situation. Using this, I develop a causal analogue to safety, where one’s belief in _p_ is causally safe if it is true in all causally relevant worlds where one believes _p_. Causal safety, I argue, can better explain the cases safety is meant to address and can avoid the arguments raised against the necessity of safety. (shrink)

This work uses microgenetic study of classroom learning to illuminate (1) the role of pre-instructional student knowledge in the construction of normative scientific knowledge, and (2) the learning mechanisms that drive change. Three enactments of an instructional sequence designed to lead to a scientific understanding of thermal equilibration are used as data sources. Only data from a scaffolded student inquiry preceding introduction of a normative model were used. Hence, the study involves nearly autonomous student learning. In two classes, (...) students developed stable and socially shared explanations (“causal schemes”) for understanding thermal equilibration. One case resulted in a near-normative understanding, while the other resulted in a non-normative “alternative conception.” The near-normative case seems to be a particularly clear example wherein the constructed causal scheme is a composition of previously documented naïve conceptions. Detailed prior description of these naive elements allows a much better than usual view of the corresponding details of change during construction of the new scheme. A list of candidate mechanisms that can account for observed change is presented. The non-normative construction seems also to be a composition, albeit of a different structural form, using a different (although similar) set of naïve elements. This article provides one of very few high-resolution process analyses showing the productive use of naïve knowledge in learning. (shrink)

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, (...) given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)