Causal selection is the task of picking out, from a field of known causally relevant factors, some factors as elements of an explanation. The Causal Parity Thesis in the philosophy of biology challenges the usual ways of making such selections among different causes operating in a developing organism. The main target of this thesis is usually gene centrism, the doctrine that genes play some special role in ontogeny, which is often described in terms of information-bearing or programming. This (...) paper is concerned with the attempt of confronting the challenge coming from the Causal Parity Thesis by offering principles of causal selection that are spelled out in terms of an explicit philosophical account of causation, namely an interventionist account. I show that two such accounts that have been developed, although they contain important insights about causation in biology, nonetheless fail to provide an adequate reply to the Causal Parity challenge: Ken Waters's account of actual-difference making and Jim Woodward's account of causal specificity. A combination of the two also doesn't do the trick, nor does Laura Franklin-Hall's account of explanation (in this volume). We need additional conceptual resources. I argue that the resources we need consist in a special class of counterfactual conditionals, namely counterfactuals the antecedents of which describe biologically normal interventions. (shrink)

Causal selection is the task of picking out, from a field of known causally relevant factors, some factors as elements of an explanation. The Causal Parity Thesis in the philosophy of biology challenges the usual ways of making such selections among different causes operating in a developing organism. The main target of this thesis is usually gene centrism, the doctrine that genes play some special role in ontogeny, which is often described in terms of information-bearing or programming. This (...) paper is concerned with the attempt of confronting the challenge coming from the Causal Parity Thesis by offering principles of causal selection that are spelled out in terms of an explicit philosophical account of causation, namely an interventionist account. I show that two such accounts that have been developed, although they contain important insights about causation in biology, nonetheless fail to provide an adequate reply to the Causal Parity challenge: Ken Waters's account of actual-difference making and Jim Woodward's account of causal specificity. A combination of the two also doesn't do the trick, nor does Laura Franklin-Hall's account of explanation (in this volume). We need additional conceptual resources. I argue that the resources we need consist in a special class of counterfactual conditionals, namely counterfactuals the antecedents of which describe biologically normal interventions. (shrink)

In psychiatry, pharmacological drugs play an important experimental role in attempts to identify the neurobiological causes of mental disorders. Besides being developed in applied contexts as potential treatments for patients with mental disorders, pharmacological drugs play a crucial role in research contexts as experimental instruments that facilitate the formulation and revision of neurobiological theories of psychopathology. This paper examines the various epistemic functions that pharmacological drugs serve in the discovery, refinement, testing, and elaboration of neurobiological theories of mental disorders. I (...) articulate this thesis with reference to the history of antipsychotic drugs and the evolution of the dopamine hypothesis of schizophrenia in the second half of the twentieth century. I argue that interventions with psychiatric patients through the medium of antipsychotic drugs provide researchers with information and evidence about the neurobiological causes of schizophrenia. This analysis highlights the importance of pharmacological drugs as research tools in the generation of psychiatric knowledge and the dynamic relationship between practical and theoretical contexts in psychiatry. (shrink)

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles (...) of counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)

This article examines how specific realist and projectivist versions of manipulability theories of causation deal with the problem of objectivity. Does an agent-dependent concept of manipulability imply that conflicting causal claims made by agents with different capacities can come out as true? In defence of the projectivist stance taken by the agency view, I argue that if the agent’s perspective is shown to be uniform across different agents, then the truth-values of causal claims do not vary arbitrarily and, (...) thus, reach a satisfactory level of objectivity. My argument connects Price’s considerations on the situation of deliberation, whose structure, common to all agents, is the same with respect to both decision making and causal claims on a concept inspired by Douglas’s classification of objectivity of thought processes: the perspective of the detached agent. I further argue that, despite his agent-independent concept of intervention, Woodward’s claim of a stronger objectivity standard cannot be achieved, as the relativity of causal concepts to a variable set brings about the issue of the agent’s choice of variables. Consequently, a more permissive objectivity standard applies to both views. (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)

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production. I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: (...) regularities under study; ‘intervention systems’, which are measurement/experimental systems; and new technological systems. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the (...) impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

Causal exclusion arguments are taken to threaten the autonomy of the special sciences, and the causal efficacy of mental properties. A recent line of response to these arguments has appealed to “independently plausible” and “well grounded” theories of causation to rebut key premises. In this paper I consider two papers which proceed in this vein and show that they share a common feature: they both require causes to be proportional to their effects. I argue that this feature is (...) a bug, and one that generalises: any attempt to rescue the autonomy of the special sciences, or the efficacy of the mental, from exclusion worries had better not look to proportionality for help. (shrink)

In Making Things Happen, James Woodward influentially combines a causal modeling analysis of actual causation with an interventionist semantics for the counterfactuals encoded in causal models. This leads to circularities, since interventions are defined in terms of both actual causation and interventionist counterfactuals. Circularity can be avoided by instead combining a causal modeling analysis with a semantics along the lines of that given by David Lewis, on which counterfactuals are to be evaluated with respect to worlds in (...) which their antecedents are realized by miracles. I argue, pace Woodward, that causal modeling analyses perform just as well when combined with the Lewisian semantics as when combined with the interventionist semantics. Reductivity therefore remains a reasonable hope. (shrink)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. (...) We then consider three key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library. (shrink)

Mere capacity views hold that agents who can intervene in an unfolding movement are performing an agentially controlled action, regardless of whether they do intervene. I introduce a simple argument to show that the noncausal explanation offered by mere capacity views fails to explain both control and action. In cases where bodily subsystems, rather than the agent, generate control over a movement, agents can often intervene to override non-agential control. Yet, contrary to what capacity views suggest, in these cases, this (...) capacity to intervene does not amount to agential control or action. I illustrate this with a case study of how passive breathing, a mere behavior, is misclassified by mere capacity views. I end by revisiting the central alternative to mere capacity views: causal control views. Advances in our understanding of how agents exert control over unfolding movements indicate that the nature of control is characterized by ubiquitous, small-scale causal interventions. (shrink)

To be effective, a medical intervention must improve one's health by targeting a disease. The concept of disease, though, is controversial. Among the leading accounts of disease-naturalism, normativism, hybridism, and eliminativism-I defend a version of hybridism. A hybrid account of disease holds that for a state to be a disease that state must both (i) have a constitutive causal basis and (ii) cause harm. The dual requirement of hybridism entails that a medical intervention, to be deemed effective, (...) must target either the constitutive causal basis of a disease or the harms caused by the disease (or ideally both). This provides a theoretical underpinning to the two principle aims of medical treatment: care and cure. (shrink)

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness (...) of hidden assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies. (shrink)

Stephen Yablo’s notion of proportionality, despite controversies surrounding it, has played a significant role in philosophical discussions of mental causation and of high-level causation more generally. In particular, it is invoked in James Woodward’s interventionist account of high-level causation and explanation, and is implicit in a novel approach to constructing variables for causal modeling in the machine learning literature, known as causal feature learning (CFL). In this article, we articulate an account of proportionality inspired by both Yablo’s account (...) of proportionality and the CFL account of variable construction. The resulting account has at least three merits. First, it illuminates an important feature of the notion of proportionality, when it is adapted to a probabilistic and interventionist framework. The feature is that at the center of the notion of proportionality lies the concept of “determinate intervention effects.” Second, it makes manifest a virtue of (common types of) high-level causal/explanatory statements over low-level ones, when relevant intervention effects are determinate. Third, it overcomes a limitation of the CFL framework and thereby also addresses a challenge to interventionist accounts of high-level causation. (shrink)

Analyzing causation in terms of Woodward's interventionist theory and describing the structure of the world in terms of causal powers are usually regarded as quite different projects in contemporary philosophy. Interventionists aim to give an account of how causal relations can be empirically discovered and described, without committing themselves to views about what causation really is. Causal powers theorists engage in precisely the latter project, aiming to describe the metaphysical structure of the world. In this paper, I (...) argue that interventionism can benefit from incorporating considerations about causal powers. I describe a previously undiscussed problem for Woodward's definition of an intervention variable that arises when interventionist causal models include disjunctive variables. This problem is solved not by excluding disjunctive (or logically compound) variables per se, but by excluding variables whose values represent disjunctions of too different causal powers. This suggests that interventionism and causal powers theories may be less distant from each other than is often assumed. (shrink)

In biomedical measurement, biomarkers are used to achieve reliable prediction of, and useful causal information about patient outcomes while minimizing complexity of measurement, resources, and invasiveness. A biomarker is an assayable metric that discloses the status of a biological process of interest, be it normative, pathophysiological, or in response to intervention. The greatest utility from biomarkers comes from their ability to help clinicians (and researchers) make and evaluate clinical decisions. In this paper we discuss a specific methodological use (...) of clinical biomarkers in pharmacological measurement: Some biomarkers, called ‘surrogate markers’, are used to substitute for a clinically meaningful endpoint corresponding to events and their penultimate risk factors. We confront the reliability of clinical biomarkers that are used to gather information about clinically meaningful endpoints. Our aim is to present a systematic methodology for assessing the reliability of multiple surrogate markers (and biomarkers in general). To do this we draw upon the robustness analysis literature in the philosophy of science and the empirical use of clinical biomarkers. -/- After introducing robustness analysis we present two problems with biomarkers in relation to reliability. Next, we propose an intervention-based robustness methodology for organizing the reliability of biomarkers in general. We propose three relevant conditions for a robust methodology for biomarkers: (R1) Intervention-based demonstration of partial independence of modes: In biomarkers partial independence can be demonstrated through exogenous interventions that modify a process some number of “steps” removed from each of the markers. (R2) Comparison of diverging and converging results across biomarkers: By systematically comparing partially-independent biomarkers we can track under what conditions markers fail to converge in results, and under which conditions they successfully converge. (R3) Information within the context of theory: Through a systematic cross-comparison of the markers we can make causal conclusions as well as eliminate competing theories. We apply our robust methodology to currently developing Alzheimer’s research to show its usefulness for making causal conclusions. (shrink)

Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish between causes, not (...) just causes from noncauses. In particular, the specificity of drugs is precisely what underwrites their value as experimental interventions on biological processes. (shrink)

Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent. According to Pearl (2009), understanding a causal relation involves multiple, hierarchically organized (...) conceptual dimensions: prediction, intervention, and counterfactual dependence. In a series of experiments, we test the hypothesis that these conceptual dimensions are differentially encoded in indicative and counterfactual conditionals. If this hypothesis holds, then there are limits as to how much of a causal relation is captured by indicative conditionals alone. Our results show that the acceptance of indicative and counterfactual conditionals can become dissociated. Furthermore, it is found that the acceptance of both is needed for accepting a causal relation between two co-occurring events. The implications that these findings have for the hypothesis above, and for recent debates at the intersection of the psychology of reasoning and causal judgment, are critically discussed. Our findings are consistent with viewing indicative conditionals as answering predictive queries requiring evidential relevance (even in the absence of direct causal relations). Counterfactual conditionals in contrast target causal relevance, specifically. Finally, we discuss the implications our results have for the yet unsolved question of how reasoners succeed in constructing causal models from verbal descriptions. (shrink)

Actions that are intended to produce harmful consequences can fail to achieve their desired effects in numerous ways. We refer to action sequences in which harmful intentions are thwarted as deviant causal chains. The culpable control model of blame (CCM)is a useful tool for predicting and explaining the attributions that observers make of the actors whose harmful intentions go awry. In this paper, we describe six types of deviant causal chains; those in which: an actor’s attempt is obviated (...) by the intervention of another person or the environment; the intended effects could not have been produced regardless of the actor’s behavior; other causes diminish the actor’s causal role; the actor brings about foreseen but undesired consequences as a result of pursuing his or her focal goal; the focal action produces a chain of increasingly remote causal events; and the actor derives unforeseen benefits from his or her nefarious actions. A basic assumption of the CCM in these cases is that attributions for the participants’ actions will depend on positive and negative evaluations of their intentions and behaviors. We describe empirical findings that are consistent with this assumption, and predict other findings for causal deviance phenomena that have not yet been investigated empirically. (shrink)

Among the many causes of an event, how do we distinguish the important ones? Are there ways to distinguish among causes on principled grounds that integrate both practical aims and objective knowledge? Psychologist Tania Lombrozo has suggested that causal explanations “identify factors that are ‘exportable’ in the sense that they are likely to subserve future prediction and intervention” (Lombrozo 2010, 327). Hence portable causes are more important precisely because they provide objective information to prediction and intervention as (...) practical aims. However, I argue that this is only part of the epistemology of causal selection. Recent work on portable causes has implicitly assumed them to be portable within the same causal system at a later time. As a result, it has appeared that the objective content of causal selection includes only facts about the causal structure of that single system. In contrast, I present a case study from systems biology in which scientists are searching for causal factors that are portable across rather than within causal systems. By paying careful attention to how these biologists find portable causes, I show that the objective content of causal selection can extend beyond the immediate systems of interest. In particular, knowledge of the evolutionary history of gene networks is necessary for correctly identifying causal patterns in these networks that explain cellular behavior in a portable way. (shrink)

The profound changes in personality, mood, and other features of the self that neural interventions can induce can be disconcerting to patients, their families, and caregivers. In the neuroethical debate, these concerns are often addressed in the context of possible threats to the narrative self. In this paper, I argue that it is necessary to consider a dimension of impacts on the narrative self which has so far been neglected: neural interventions can lead to a loss of meaning of actions, (...) feelings, beliefs, and other intentional elements of our self-narratives. To uphold the coherence of the self-narrative, the changes induced by neural interventions need to be accounted for through explanations in intentional or biochemical terms. However, only an explanation including intentional states delivers the content to directly ascribe personal meaning, i.e., subjective value to events. Neural interventions can deprive events of meaning because they may favor a predominantly biochemical account. A loss of meaning is not inherently negative but it can be problematic, particularly if events are affected one was not prepared or willing to have stripped of meaning. The paper further examines what it is about neural interventions that impacts meaning by analyzing different methods. To which degree the pull towards a biochemical view occurs depends on the characteristics of the neural intervention. By comparing Deep Brain Stimulation, Prozac, Ritalin, psychedelics, and psychotherapy, the paper identifies some main factors: the rate of change, the transparency of the causal chain, the involvement of the patient, and the presence of an acute phenomenological experience. (shrink)

The problem of mental causation is discussed by taking into account some recent developments in the philosophy of science. The problem is viewed from the perspective of the new interventionist theory of causation developed by Woodward. The import of the idea that causal claims involve contrastive classes in mental causation is also discussed. It is argued that mental causation is much less a problem than it has appeared to be.

It has been stated that the notion of cause and effect is one object of study that sciences and engineering revolve around. Lately, in software engineering, diagrammatic causal inference methods (e.g., Pearl’s model) have gained popularity (e.g., analyzing causes and effects of change in software requirement development). This paper concerns diagrammatical (graphic) models of causal relationships. Specifically, we experiment with using the conceptual language of thinging machines (TMs) as a tool in this context. This would benefit works on (...) causal relationships in requirements engineering, enhance our understanding of the TM modeling, and contribute to the study of the philosophical notion of causality. To specify the causality in a system’s description is to constrain the system’s behavior and thus exclude some possible chronologies of events. The notion of causality has been studied based on tools to express causal questions in diagrammatic and algebraic forms. Causal models deploy diagrammatic models, structural equations, and counterfactual and interventional logic. Diagrammatic models serve as a language for representing what we know about the world. The research methodology in the paper focuses on converting causal graphs into TM models and contrasts the two types of representation. The results show that the TM depiction of causality is more complete and therefore can provide a foundation for causal graphs. (shrink)

Recent years have seen growing interest in modifying interventionist accounts of causal explanation in order to characterise noncausal explanation. However, one surprising element of such accounts is that they have typically jettisoned the core feature of interventionism: interventions. Indeed, the prevailing opinion within the philosophy of science literature suggests that interventions exclusively demarcate causal relationships. This position is so prevalent that, until now, no one has even thought to name it. We call it “intervention puritanism” (I-puritanism, for (...) short). In this paper, we mount the first sustained defence of the idea that there are distinctively noncausal explanations which can be characterized in terms of possible interventions; a position we call “intervention liberalism” (I-liberalism, for short). While many have followed James Woodward (2003) in committing to I-puritanism, we trace support for I-liberalism back to the work of Jaegwon Kim (1974). Furthermore, we analyse two recent sources of scepticism regarding I-liberalism: debate surrounding mechanistic constitution; and attempts to provide a monistic account of explanation. We show that neither literature provides compelling reasons for adopting I-puritanism. Finally, we present a novel taxonomy of available positions upon the role of possible interventions in explanation: weak causal imperialism; strong causal imperialism; monist intervention puritanism; pluralist intervention puritanism; monist intervention liberalism; and finally, the specific position defended in this paper, pluralist intervention liberalism. (shrink)

Both philosophical and practical analyses of global justice issues have been vitiated by two errors: a too-high emphasis on the supposed duties of collectives to act, and a too-low emphasis on the analysis of causes and risks. Concentrating instead on the duties of individual actors and analysing what they can really achieve reconfigures the field. It diverts attention from individual problems such as poverty or refugees or questions on what states should do. Instead it shows that there are different duties (...) for political leaders, intelligence operatives, opinion leaders and citizens in devising, urging and implementing such plans as transfers of aid with accountability, military interventions in rogue states and limited intakes of refugees. With collectivist excuses for inaction such as sovereignty out of the way, it is possible to take a cautiously optimistic view of the possibility of forceful and morally responsible interventions in the range of major global problems. (shrink)

I present a reconstruction of F.H.C. Crick's two 1957 hypotheses "Sequence Hypothesis" and "Central Dogma" in terms of a contemporary philosophical theory of causation. Analyzing in particular the experimental evidence that Crick cited, I argue that these hypotheses can be understood as claims about the actual difference-making cause in protein synthesis. As these hypotheses are only true if restricted to certain nucleic acids in certain organisms, I then examine the concept of causal specificity and its potential to counter claims (...) about causal parity of DNA and other cellular components. I first show that causal specificity is a special kind of invariance under interventions, namely invariance of generalizations that range over finite sets of discrete variables. Then, I show that this notion allows the articulation of a middle ground in the debate over causal parity. (shrink)

In order to support health interventions, biomedical and population health researchers need to collect solid evidence. This article asks what type of evidence this should be and expands on previous work that focused on etiological explanations, or causal-mechanical explanations of why and how illness occurs. The article proposes adding predictive evidence to the explanatory evidence, in order to form a joint evidence set, or JES = [A,B,C,D], which consists of four different types of evidence: association [A], biology [B], confirmation (...) [C], and difference-making [D]. The article discusses explanatory coherence as a backbone for this proposal, suggests criteria for each type of evidence, and offers a rubric for multi-evidence mapping. (shrink)

In the last 20 years, a stream of research emerged under the label of „complex problem solving“ (CPS). This research was intended to describe the way people deal with complex, dynamic, and intransparent situations. Complex computer-simulated scenarios were as stimulus material in psychological experiments. This line of research lead to subtle insights into the way how people deal with complexity and uncertainty. Besides these knowledge-rich, realistic, intransparent, complex, dynamic scenarios with many variables, a second line of research used more simple, (...) knowledge-lean scenarios with a low number of variables („minimal complex systems“, MCS) that have been proposed recently in problem-solving research for the purpose of educational assessment. In both cases, the idea behind the use of microworlds is to increase validity of problem solving tasks by presenting interactive environments that can be explored and controlled by participants while pursuing certain action goals. The main argument presented here is: both types of systems - CPS and MCS – can only be dealt with successfully if causal dependencies between input and output variables are identified and used for system control. System knowledge is necessary for control and intervention. But CPS and MCS differ in their way of how causal dependencies are identified and how the mental model is constructed; therefore, they cannot be compared directly to each other with respect to the cognitive processes that are necessary for solving the tasks. Knowledge-poor MCS tasks address only a small fraction of the cognitive processes and structures needed for knowledge-rich CPS situations. (shrink)

Recent breakthroughs in artificial intelligence have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable ``black boxes,'' making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with a (...) focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path towards more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience. (shrink)

In this volume, leading philosophers of psychiatry examine psychiatric classification systems, including the Diagnostic and Statistical Manual of Mental Disorders, asking whether current systems are sufficient for effective diagnosis, treatment, and research. Doing so, they take up the question of whether mental disorders are natural kinds, grounded in something in the outside world. Psychiatric categories based on natural kinds should group phenomena in such a way that they are subject to the same type of causal explanations and respond similarly (...) to the same type of causal interventions. When these categories do not evince such groupings, there is reason to revise existing classifications. The contributors all question current psychiatric classifications systems and the assumptions on which they are based. They differ, however, as to why and to what extent the categories are inadequate and how to address the problem. Topics discussed include taxometric methods for identifying natural kinds, the error and bias inherent in DSM categories, and the complexities involved in classifying such specific mental disorders as "oppositional defiance disorder" and pathological gambling. -/- Contributors George Graham, Nick Haslam, Allan Horwitz, Harold Kincaid, Dominic Murphy, Jeffrey Poland, Nancy Nyquist Potter, Don Ross, Dan Stein, Jacqueline Sullivan, Serife Tekin, Peter Zachar. (shrink)

An account of what sort of causal integration is necessary for an agent to exercise agency is offered in support of a soft-line response to Derk Pereboom’s four-case argument against source-compatibilism. I argue that, in cases of manipulation, the manipulative activity affects the identity of the causal process of which it is a part. Specifically, I argue that causal processes involving direct manipulation fail to count as exercises of intentional agency because they involve heteromesial causal deviance. (...) In contrast, standard deterministic causal processes do not involve heteromesial causal deviance and are agency-preserving. The upshot is that there is a relevant difference between a causal process involving direct manipulation by another agent and a deterministic causal process that involves no such intervention. If this is right, then Pereboom’s four-case argument does not pose a threat to source-compatibilist theories of free will and moral responsibility. (shrink)

Philosophers have recently highlighted substantial affinities between causation and grounding, which has inclined some to import the conceptual and formal resources of causal interventionism into the metaphysics of grounding. The prospect of grounding interventionism raises two important questions: exactly what are grounding interventions, and why should we think they enable knowledge of grounding? This paper will approach these questions by examining how causal interventionists have addressed (or might address) analogous questions and then comparing the available options for grounding (...) interventionism. I argue that grounding interventions must be understood in worldly terms, as adding something to or deleting something from the roster of entities, or making some fact obtain or fail to obtain. I consider three bases for counterfactual assessment: imagination, structural equation models, and background theory. I conclude that grounding interventionism requires firmer epistemological foundations, without which the interventionist's epistemology of grounding is incomplete and ineffectually rationalist. (shrink)

While structural equations modeling is increasingly used in philosophical theorizing about causation, it remains unclear what it takes for a particular structural equations model to be correct. To the extent that this issue has been addressed, the consensus appears to be that it takes a certain family of causal counterfactuals being true. I argue that this account faces difficulties in securing the independent manipulability of the structural determination relations represented in a correct structural equations model. I then offer an (...) alternate understanding of structural determination, and I demonstrate that this theory guarantees that structural determination relations are independently manipulable. The account provides a straightforward way of understanding hypothetical interventions, as well as a criterion for distinguishing hypothetical changes in the values of variables which constitute interventions from those which do not. It additionally affords a semantics for causal counterfactual conditionals which is able to yield a clean solution to a problem case for the standard ‘closest possible world’ semantics. (shrink)

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of inference. (...) Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence. (shrink)

Causation has always been a philosophically controversial subject matter. While David Hume’s empiricist account of causation has been the dominant influence in analytic philosophy and science during modern times, a minority view has instead connected causation essentially to agency and manipulation. A related approach has for the first time gained widespread popularity in recent years, due to new powerful theories of causal inference in science that are based in a technical notion of intervention, and James Woodward’s closely connected (...) interventionist theory of causation in philosophy. This monograph assesses five manipulationist or interventionist theories of causation, viewed as theories that purport to tell us what causation is by providing us with the meaning of causal claims. It is shown that they cannot do this, as the conditions on causation that they impose are too weak, mainly due to ineliminable circularities in their definitions of causal terms. It is then argued that a subset of Woodward’s theory can nevertheless contribute crucially to an explanation of the unique role that manipulation has in our acquisition of causal knowledge. This explanation differs from the common regularist explanation of the epistemic utility of manipulation and experiment, and it is taken to confirm several important manipulationist intuitions. However, the success of the explanation depends on (this subset of) interventionism not itself being understood as a theory of causation, but as a theory of intervention. (shrink)

Why and how do norms emerge? Which norms emerge and why these ones in particular? Such questions belong to the ‘problem of the emergence of norms’, which consists of an inquiry into the production of norms in social collectives. I address this question through the ethnographic study of the emergence of ‘norms against violence’ in the political collective Occupy Geneva. I do this, first, empirically, with the analysis of my field observations; and, second, theoretically, by discussing my findings. In consequence (...) of two episodes categorized as sexual assaults that occurred in their camp, the members of Occupy Geneva decided to tackle those issues in a general assembly. Their goal was to amend their first charter of good conduct in order to reform its norms and complete it with norms aiming to regulate ‘facts’ of ‘unjustified violence’. During a collective deliberation, new norms were devised, debated and consensually adopted. The writing of the new charter took place in a second general assembly during which the wording of the written norms was collectively decided. I show that indignation over the sexual assaults was the main motive that led to the collective deliberation, and that the entire process of the making of these norms was characterized by different collective emotions. Indeed, indignation, contempt and fear played major roles in the emergence of norms prohibiting violence, allowing punishment and exclusion of wrongdoers, and prescribing collective intervention against an aggressor to neutralize the threat represented. These findings prompt me to hypothesize that social norms emerge from emotions thanks to the latters’ internal structure; and that emotions provide causal and grounding explanations for this emergence. Thus, emotions allow us to answer the questions: ‘Why do norms emerge?’ and ‘Why do they have their specific forms?’ In short, I argue that social norms have emotional foundations. (shrink)

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize (...) and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. (shrink)

A radically dualist view of the relationship between God and the universe is apt to make the problem of Divine intervention more difficult than under other metaphysical conceptions. We need to find a closer relationship than this if the causal picture is to work. We could try saying that God is realized by the universe, without being reducible to the universe. He has no further substance over and above that of the universe, but he is not simply identical (...) to the universe. I am not sure I know what this idea of realization comes to for the case of God and the universe, but it least it promises to make it feasible for God to be enmeshed in the natural causal order, without collapsing into it. It is not so much that God intervenes as supervenes, to use the jargon. On this picture, there is a mega- universe that includes both the physical universe and God, with the two locked somehow together. (shrink)

While practitioners think highly of randomized studies, some philosophers argue that there is no epistemic reason to randomize. Here I show that their arguments do not entail their conclusion. Moreover, I provide novel reasons for randomizing in the context of interventional studies. The overall discussion provides a unified framework for assessing baseline balance, one that holds for interventional and observational studies alike. The upshot: practitioners’ strong preference for randomized studies can be defended in some cases, while still offering a nuanced (...) approach to evidence-appraisal, one where not all non-randomized studies are treated equally. (shrink)

This paper discusses methodological similarities between Collingwood's approach to causation and contemporary manipulability-based views. Firstly, I argue that on both approaches there is a preoccupation with the origin of causal concepts which further connects to the aim of establishing the priority of a certain concept/sense of causation as more fundamental. The significant difference lies in Collingwood's focus on the logical and historical priority (Collingwood's sense I) while in more recent theories the focus has been on psychology (i.e., on different (...) philosophical concepts of causation tested in psychological setting-- Woodward, Waskan, Gijsbers & de Bruin). Secondly, I argue that on both Collingwood's views and contemporary approaches (especially Menzies & Price, Price, but also Woodward) there is an important connection between causation and the agent's perspective. This can be subsumed under what Collingwood identifies as the main presupposition underlying causal thought: the relation between the agent's perceived situation and the agent's desired situation/ goal. More broadly, the recent views on causation under discussion share Collingwood's aim of elucidating the problem of causation through investigating causal knowledge and thus can be integrated with his wider project of addressing metaphysical issues through the means of epistemology. (shrink)

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

Optogenetic techniques are described as “revolutionary” for the unprecedented causal control they allow neuroscientists to exert over neural activity in awake behaving animals. In this paper, I demonstrate by means of a case study that optogenetic techniques will only illuminate causal links between the brain and behavior to the extent that their error characteristics are known and, further, that determining these error characteristics requires comparison of optogenetic techniques with techniques having well known error characteristics and consideration of the (...) broader neural and behavioral context in which the targets of optogenetic interventions are situated. (shrink)

When approaching the nature and etiology of psychophysical problems in a given population, it is important to understand the causal relations, the mechanisms, and the processes which underlie given problems, issues, and disorders. This examination attempts to investigate the connection between neural pathophysiology and proprioceptive ontology of olfactory and visual cortical circuitry on the basis of the current scientific evidence indicated by the results of studies in which subjects underwent a series of therapeutic modalities focused on the improvement of (...) overall wellbeing with the utilization of devices and technologies to stimulate multiple motor vs. somatosensory processes. More specifically, the studies at the centers of this analysis incorporated physical movement and virtual reality interactions, with the supplement of olfactory stimulation. In this examination, such research is also investigated from a theoretical standpoint, with the ultimate goal to better determine which interventions are justified under the premise of a mutual support between empirical values (through laboratory examination) and logical deduction (via philosophical investigation). (shrink)

Even though the evidence‐based medicine movement (EBM) labels mechanisms a low quality form of evidence, consideration of the mechanisms on which medicine relies, and the distinct roles that mechanisms might play in clinical practice, offers a number of insights into EBM itself. In this paper, I examine the connections between EBM and mechanisms from several angles. I diagnose what went wrong in two examples where mechanistic reasoning failed to generate accurate predictions for how a dysfunctional mechanism would respond to (...) class='Hi'>intervention. I then use these examples to explain why we should expect this kind of mechanistic reasoning to fail in systematic ways, by situating these failures in terms of evolved complexity of the causal system(s) in question. I argue that there is still a different role in which mechanisms continue to figure as evidence in EBM: namely, in guiding the application of population‐level recommendations to individual patients. Thus, even though the evidence‐based movement rejects one role in which mechanistic reasoning serves as evidence, there are other evidentiary roles for mechanistic reasoning. This renders plausible the claims of some critics of evidencebased medicine who point to the ineliminable role of clinical experience. Clearly specifying the ways in which mechanisms and mechanistic reasoning can be involved in clinical practice frames the discussion about EBM and clinical experience in more fruitful terms. (shrink)

In this paper, Isuggest that placebo effects, as we know them today, should be understood as experimental phenomena, low-level regularities whose causal structure is grasped through particular experimental designs with little theoretical guidance. Focusing on placebo interventions with needles for pain reduction -one of the few placebo regularities that seems to arise in meta-analytical studies- I discuss the extent to which it is possible to decompose the different factors at play through more fine-grained randomized clinical trials. My sceptical argument (...) is twofold. On the one hand, I argue that experiments alone are not enough to standardize interventions, and that it is necessary to include theories. On the other hand, I argue that the social interactions that seem to be part of placebo effects are difficult, if not impossible, to blind. Therefore, the measurement biases arising from the participants’ reactivity to the experimental setup cannot be controlled for. Further decomposition of placebo effects requires a theoretical account of the existing experimental regularities that may guide further tests. (shrink)

Probably the most distinctive feature of synthetic biology is its being “synthetic” in some sense or another. For some, synthesis plays a unique role in the production of knowledge that is most distinct from that played by analysis: it is claimed to deliver knowledge that would otherwise not be attained. In this contribution, my aim is to explore how synthetic biology delivers knowledge via synthesis, and to assess the extent to which this knowledge is distinctly synthetic. On the basis of (...) distinctions between knowledge-how and knowledge-why, and between syntheses that succeed and syntheses that fail, I argue that the contribution of synthesis to knowledge is best understood when syntheses are construed as experimental interventions that aim at probing causal relationships between properties of the entities that are combined through these syntheses and properties of their target products. The distinctiveness of synthetic biology in its quest for knowledge through synthesis stems from its ability to sample at will a space of empirical possibilities that is not only huge but also that has been so scarcely sampled by nature. (shrink)

Traces of many past events are often layered or superposed, in brain, body, and world alike. This often poses challenges for individuals and groups, both in accessing specific past events and in regulating or managing coexisting emotions or attitudes. We sometimes struggle, for example, to find appropriate modes of engagement with places with complex and difficult pasts. More generally, there can appear to be a tension between what we know about the highly constructive nature of remembering, whether it is drawing (...) on neural or worldly resources or both, and the ways that we need and use memory to make claims on the past, and to maintain some appropriate causal connections to past events. I assess the current state of work on situated affect and distributed memory, and the recent criticisms of the ‘dogma of harmony’ in these fields. I then deploy these frameworks to examine some affective dimensions of place memory, sketching a strongly distributed conception of places as sometimes partly constituting the processes and activities of feeling and remembering. These approaches also offer useful perspectives on the problems of how to engage – politically and aesthetically – with difficult pasts and historically burdened heritage. In assessing artistic interventions in troubled places, we can seek responsibly to do justice to the past while fully embracing the dynamic and contested constructedness of our present emotions, memories, and activities. (shrink)

The COVID-19 pandemic has demonstrated how different (even conflicting) interventions on nature can be scientifically justified: interventions can be deemed "effective" only in relation to specific target variables - in relation to variables the values of which we seek to control. Choosing the "right" target variables, in turn, depends on our values and pragmatic aims. This essay is based on a presentation given at the symposium "Multidisciplinary Perspectives on the COVID-19 Pandemic", organised at the Helsinki Collegium for Advanced Studies on (...) 16 June 2020. (shrink)

Cognitive Behavioral Therapy has become the dominant form of psychotherapy in North America. The CBT model is theoretically based on the idea that all external and internal stimuli are filtered through meaning-making, consciously accessible cognitive schemas. The goal of CBT is to identify dysfunctional or maladaptive thoughts and beliefs, and replace them with more adaptive cognitive interpretations. While CBT is clearly effective as a treatment, there is good reason to be skeptical that its efficacy is due to the causal (...) mechanisms posited by the CBT model. This paper will argue that the specific cognitive schemas posited by the CBT model likely do not play a direct role in the development or treatment of psychological illness. Cognitive schemas, as identified in CBT interventions, are likely to be the result of patient confabulation and epistemically under-supported practitioner-based identification. CBT interventions appear to impose coherence on patients’ psychological states, rather than identifying and modifying preexistent causally efficacious core beliefs. (shrink)