This book offers a discussion about how people think, talk, learn, and explain things in causal terms in terms of action and manipulation. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning.

In his recent book, Causation: A Realistic Approach , Michael Tooley discusses the following thesis, which he calls the ‘thesis of the Humean Supervenience of Causal Relations’: The truth values of all singular causal statements are logically determined by the truth values of statements of causal laws, together with the truth values of non-causal statements about particulars . represents one version of the ‘Humean’ idea that there is no more factual content to the claim that two (...) particular events are causally connected than that they occur, instantiate some law or regularity, and perhaps bear some appropriate non-causal to each other. This is an idea that is tacitly or explicitly assumed in most familiar accounts of singular causal statements. For example is assumed by many probabilistic theories of singular causal statements, by theories which attempt to analyse singular causal statements in terms of necessary and sufficient conditions, and, as I shall argue below, by David Lewis' counterfactual theory. (shrink)

When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that (...) this causal superseding effect is dependent on a particular event structure, following a prediction of our counterfactual model. Experiment 4 demonstrates that causal superseding can occur with violations of non-moral norms. We propose a model of the superseding effect based on the idea of counterfactual sufficiency. (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 this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion of causal exclusion arguments in (...) the light of an interventionist theory of causation such as Woodward's (2003) and discuss a possible objection to my causal Bayes net reconstruction. (shrink)

This book, by one of the pre-eminent philosophers of science writing today, offers the most comprehensive account available of causal asymmetries. Causation is asymmetrical in many different ways. Causes precede effects; explanations cite causes not effects. Agents use causes to manipulate their effects; they don't use effects to manipulate their causes. Effects of a common cause are correlated; causes of a common effect are not. This book explains why a relationship that is asymmetrical in one of these regards is (...) asymmetrical in the others. Hausman discovers surprising hidden connections between theories of causation and traces them all to an asymmetry of independence. This is a major book for philosophers of science that will also prove insightful to economists and statisticians. (shrink)

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships (...) to disease. When Koch’s postulates are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made. (shrink)

Despite the voluminous literature on biological functions produced over the last 40 years, few philosophers have studied the concept of function as it is used in neuroscience. Recently, Craver (forthcoming; also see Craver 2001) defended the causal role theory against the selected effects theory as the most appropriate theory of function for neuroscience. The following argues that though neuroscientists do study causal role functions, the scope of that theory is not as universal as claimed. Despite the strong prima (...) facie superiority of the causal role theory, the selected effects theory (when properly developed) can handle many cases from neuroscience with equal facility. It argues this by presenting a new theory of function that generalizes the notion of a ‘selection process’ to include processes such as neural selection, antibody selection, and some forms of learning—that is, to include structures that have been differentially retained as well as those that have been differentially reproduced. This view, called the generalized selected effects theory of function, will be defended from criticism and distinguished from similar views in the literature. (shrink)

We can perceive not only low-level features of events such as color and motion, but also seemingly higher-level properties such as causality. A prototypical example of causal perception is the ”launching effect’: one object moves toward a stationary second object until they are adjacent, at which point A stops and B starts moving in the same direction. Beyond these motions themselves --- and regardless of any higher-level beliefs --- this display induces a vivid visual impression of causality, wherein A (...) is seen to cause B’s motion. Do such percepts reflect a unitary category of visual processing, or might there be multiple distinct forms of causal perception? While launching is often simply equated with causal perception, researchers have sometimes described other phenomena such as ”triggering’ and ”entraining’. We used psychophysical methods to determine whether these labels really carve visual processing at its joints, and how putatively different forms of causal perception relate to each other. Previous research demonstrated retinotopically specific adaptation to causality: exposure to causal launching makes subsequent ambiguous events in that same location more likely to be seen as non-causal ”passing’. Here, after replicating this effect, we show that exposure to triggering also yields retinotopically specific adaptation for subsequent ambiguous launching displays, but that exposure to entraining does not. Collectively, these results reveal that visual processing distinguishes some types of causal interactions. (shrink)

The counterfactual approach to explainable AI (XAI) seeks to provide understanding of AI systems through the provision of counterfactual explanations. In a recent systematic review, Chou et al. (Inform Fus 81:59–83, 2022) argue that the counterfactual approach does not clearly provide causal understanding. They diagnose the problem in terms of the underlying framework within which the counterfactual approach has been developed. To date, the counterfactual approach has not been developed in concert with the approach for specifying causes developed by (...) Pearl (Causality: Models, reasoning, and inference. Cambridge University Press, 2000) and Woodward (Making things happen: A theory of causal explanation. Oxford University Press, 2003). In this paper, I build on Chou et al.’s work by applying the Pearl-Woodward approach. I argue that the standard counterfactual approach to XAI is capable of delivering causal understanding, but that there are limitations on its capacity to do so. I suggest a way to overcome these limitations. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

In the last two decades few topics in philosophy of science have received as much attention as mechanistic explanation. A significant motivation for these accounts is that scientists frequently use the term “mechanism” in their explanations of biological phenomena. While scientists appeal to a variety of causal concepts in their explanations, many philosophers argue or assume that all of these concepts are well understood with the single notion of mechanism. This reveals a significant problem with mainstream mechanistic accounts– although (...) philosophers use the term “mechanism” interchangeably with other causal concepts, this is not something that scientists always do. This paper analyses two causal concepts in biology–the notions of “mechanism” and “pathway”–and how they figure in biological explanation. I argue that these concepts have unique features, that they are associated with distinct strategies of causal investigation, and that they figure in importantly different types of explanation. (shrink)

Causal descriptivism and its relative nominal descriptivism are critically examined. It is argued that they do not manage to undermine the principal conclusions of the new theory of reference.

This paper argues that the non-reductive monist need not be concerned about the ‘problem’ of mental causation; one can accept both the irreducibility of mental properties to physical properties and the causal closure of the physical. More precisely, it is argued that instances of mental properties can be causally efficacious, and that there is no special barrier to seeing mental properties whose instances are causally efficacious as being causally relevant to the effects they help to bring about. It is (...) then shown that the causal relevance of mental properties is consistent with there being no downward causation, so the dilemma of ‘epiphenomenalism or reduction’ can be avoided. Non-reductive monism lives on as a viable position in the philosophy of mind. (shrink)

If physical reality is nonseparable, as quantum mechanics suggests, then it may contain processes of a quite novel kind. Such nonseparable processes could connect space-like separated events without violating relativity theory or any defensible locality condition. Appeal to nonseparable processes could ground theoretical explanations of such otherwise puzzling phenomena as the two-slit experiment, and EPR- type correlations. We find such phenomena puzzling because they threaten cherished conceptions of how causes operate to produce their effects. But nonseparable processes offer us an (...) alternative deal of natural order, conformity to which makes such phenomena seem quite normal and not at all unexpected. Attempts to answer the further question, as to whether an appeal to a nonseparable process provides a genuine "causal" explanation, have something to teach us about our concept of causation, but do not threaten to undermine the value of the explanation itself. (shrink)

We use concepts of causal powers and their relatives-dispositions, capacities, and abilities-to describe the world around us, both in everyday life and in scientific practice. This volume presents new work on the nature of causal powers, and their connections with other phenomena within metaphysics, philosophy of science, and philosophy of mind.

An understanding of cause--effect relationships is fundamental to the study of cognition. In this book, outstanding specialists from comparative psychology, social psychology, developmental psychology, anthropology, and philosophy present the newest developments in the study of causal cognition and discuss their different perspectives. They reflect on the role and forms of causal knowledge, both in animal and human cognition, on the development of human causal cognition from infancy, and on the relationship between individual and cultural aspects of (...) class='Hi'>causal understanding. The result is a state-of-the-art, informative, insightful, and interdisciplinary debate aimed at the non-specialist. (shrink)

This paper attempts to set up a probabilistic framework for understanding the notion of causal necessity. What results is a relaxed and relativized probabilistic theory of epsilon-Causal necessity and an explicit attempt to avoid deterministic assumptions. The theory developed emphasizes the notions of partial cause, Causal contribution, And the degree of contribution. Implications for causal overdetermination, Causal preemption, And causal discourse are discussed.

According to one conception of strong emergence, strongly emergent properties are nomologically necessitated by their base properties and have novel causal powers relative to them. In this paper, I raise a difficulty for this conception of strong emergence, arguing that these two features are incompatible. Instead of presenting this as an objection to the friends of strong emergence, I argue that this indicates that there are distinct varieties of strong emergence: causal emergence and epiphenomenal emergence. I then explore (...) the prospects of emergentism with this distinction in the background. (shrink)

Causal decision theory is one of our most prominent theories of rational choice and the “why ain’cha rich?” argument is one of the most prominent objections to this theory. According to WAR, CDT is not an adequate theory of rational choice because it leads agents to make decisions that foreseeably leave them less well off than agents that decide in some other manner. Some philosophers take WAR to decisively undermine CDT. On the other hand, others take WAR to fail (...) to resolve the debate over CDT’s adequacy. In this paper, I will defend this second view: WAR does not resolve the debate at hand but instead leads to deadlock. Then, in the second half of this paper, I will show that this deadlock is not broken by a recent variant on WAR due to Caspar Hare and Brian Hedden. Not only does this result have implications for the debate over CDT’s adequacy but this discussion also casts light on the broader success/rationality link. (shrink)

Proponents of causal decision theories argue that classical Bayesian decision theory (BDT) gives the wrong advice in certain types of cases, of which the clearest and commonest are the medical Newcomb problems. I defend BDT, invoking a familiar principle of statistical inference to show that in such cases a free agent cannot take the contemplated action to be probabilistically relevant to its causes (so that BDT gives the right answer). I argue that my defence does better than those of (...) Ellery Eells and Richard Jeffrey; and that it applies, where necessary, to other types of Newcomb problem. (shrink)

The manipulationist account of causation provides a conceptual analysis of cause-effect relationships in terms of hypothetical experiments. It also explains why and how experiments are used for the empirical testing of causal claims. This paper attempts to apply the manipulationist account of causation to a broader range of experiments—a range that extends beyond experiments explicitly designed for the testing of causal claims. I aim to show that the set of causal inferences afforded by an experiment is determined (...) solely on the basis of contrasting case structures that I call “experimental series”, and that the conditions that suffice for causal inference obtain quite commonly, even among “ordinary” experiments that are not explicitly designed for the testing of causal claims. (shrink)

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily (...) on the particular experimental setup and the assumptions that can be made. ‡The first author is funded by the Causal Learning Collaborative Initiative supported by the James S. McDonnell Foundation. Many aspects of this paper were inspired by discussions with members of the collaborative. †To contact the authors, please write to: Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213; e-mail: [email protected] and [email protected]. (shrink)

I reconstruct the discovery of the Higgs boson by the ATLAS collaboration at CERN as the application of a series of inferences from effects to causes. I show to what extent such diagnostic causal inferences can be based on well established knowledge gained in previous experiments. To this extent, causal reasoning can be used to infer the existence of entities, rather than just causal relationships between them. The resulting account relies on the principle of causality, attributes only (...) a heuristic role to the theory’s predictions, and shows how, and to what extent, data selection can be used to exclude alternative causes, even “unconceived” ones. (shrink)

Owens (1993) argues that a tension exists between our commonsense view of mental states and the scientific view that psychological explanations not contradict supervenience. He suggests that one cannot accept the anti-individualistic conclusions of Twin-Earth thought experiments and continue to use folk psychological states to explain behavior. I argue that his conclusions are based on individuating content widely and causal powers narrowly, and that such individuation violates consistency assumptions about the terms of his discussion. Thus, I argue, the tension (...) he points to evaporates when we adopt either a consistently wide view or a consistently narrow view. (shrink)

We investigate the context of discovery of two significant achievements of twentieth century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: even basic strategies can illuminate “hard cases” of scientific discovery that go (...) far beyond simple extrapolation or analogy; the causal–mechanistic approach to discovery permits a middle course between the extremes of a completely substrate-neutral and a completely domain-specific view of scientific discovery; the existing literature on mechanism discovery underemphasizes the role of combinatorial approaches in defining and exploring search spaces of possible problem solutions; there is a subtle interplay between a fine-grained mechanistic and a more coarse-grained causal level of analysis, and both are needed to make discovery processes intelligible. (shrink)

Does y obtain under the counterfactual supposition that x? The answer to this question is famously thought to depend on whether y obtains in the most similar world in which x obtains. What this notion of ‘similarity’ consists in is controversial, but in recent years, graphical causal models have proved incredibly useful in getting a handle on considerations of similarity between worlds. One limitation of the resulting conception of similarity is that it says nothing about what would obtain were (...) the causal structure to be different from what it actually is, or from what we believe it to be. In this paper, we explore the possibility of using graphical causal models to resolve counterfactual queries about causal structure by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which a graph G* can be more similar to a graph G than a graph G**, we introduce multiple similarity metrics, as well as multiple ways to prioritize the various metrics when settling counterfactual queries about causal structure. (shrink)

Recent approaches to causal modelling rely upon the causal Markov condition, which specifies which probability distributions are compatible with a directed acyclic graph. Further principles are required in order to choose among the large number of DAGs compatible with a given probability distribution. Here we present a principle that we call frugality. This principle tells one to choose the DAG with the fewest causal arrows. We argue that frugality has several desirable properties compared to the other principles (...) that have been suggested, including the well-known causal faithfulness condition. _1_ Introduction _2_ The Causal Markov Condition _3_ Faithfulness _4_ Frugality _4.1_ Basic independences and frugality _4.2_ General properties of directed acyclic graphs satisfying frugality _4.3_ Connection to minimality assumptions _5_ Frugality as a Parsimony Principle _6_ Conclusion Appendix. (shrink)

My target article (henceforth referred to as TA) presents evidence for causal interactions between consciousness and brain and some standard ways of accounting for this evidence in clinical practice and neuropsychological theory. I also point out some of the problems of understanding such causal interactions that are not addressed by standard explanations. Most of the residual problems have to do with how to cross the “explanatory gap” from consciousness to brain. I then list some of the reasons why (...) the route across this gap suggested by physicalism won't work, in spite of its current popularity in consciousness studies. My own suggested route across the explanatory gap is more subterranean, where consciousness and brain can be seen to be dual aspects of a unifying, psychophysical mind. Some of the steps on this deeper route still have to be filled in by empirical research. But (as far as I can judge) there are no gaps that cannot be filled—just a different way of understanding consciousness, mind, brain and their causal interaction, with some interesting consequences for our understanding of free will. The commentaries on TA examined many aspects of my thesis viewed from both Western and Eastern perspectives. This reply focuses on how dual-aspect monism compares with currently popular alternatives such as “nonreductive physicalism”, clarifies my own approach, and reconsiders how well this addresses the “hard” problems of consciousness. We re-examine how conscious experiences relate to their physical/functional correlates and whether useful analogies can be drawn with other, physical relationships that appear to have dual-aspects. We also examine some fundamental differences between Western and Eastern thought about whether the existence of the physical world or the existence of consciousness can be taken for granted (with consequential differences about which of these is “hard” to understand). I then suggest a form of dual-aspect Reflexive Monism that might provide a path between these ancient intellectual traditions that is consistent with science and with common sense. (shrink)

There are two concepts of causes, property causation and token causation. The principle I want to discuss describes an epistemological connection between the two concepts, which I call the Connecting Principle. The rough idea is that if a token event of type Cis followed by a token event of type E, then the support of the hypothesis that the first event token caused the second increases as the strength of the property causal relation of C to E does. I (...) demonstrate the principle, illustrate its application to phylogenies, infections, and rumours, and discuss its consequence for the conceptual distinctness of causal processes from the events they connect. Although I am by no means confident that the Connecting Principle is ultimately correct, it seems to be a useful point of departure into an important aspect of the epistemology of causality. (shrink)

The causal pairing problem allegedly renders nonphysical minds causally impotent. This article demonstrates how a dualist view I call neo-Thomistic hylomorphism can circumnavigate the causal pairing problem. After explicating the problem and hylomorphism, I provide an account of causal pairing that appeals to a foundational tenet of hylomorphism. Subsequently, I suggest that a prominent view of consciousness in theoretical neuroscience—the integrated information theory—can learn from hylomorphism and likewise account for causal pairing.

Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, (...) we introduce the problem of causal-network aggregation. Finally, we revisit the aggregation of probabilistic judgments when this is constrained by prior aggregation of qualitative causal judgments. (shrink)

The online version of the original article can be found under doi:10.1007/s11948-007-9045-2.