Considerable evidence proves that causal learning and causal understanding greatly enhance our ability to manipulate the physical world and are major factors that distinguish humans from other primates. How do we enable unintelligent robots to think causally, answer the questions raised with "why" and even understand the meaning of such questions? The solution is one of the keys to realizing artificial intelligence. Judea Pearl believes that to achieve human-like intelligence, researchers must start by imitating the intelligence of children, (...) so he proposed a "causal inference engine" to help future artificial intelligence make causal inference, pass the Minimal Turing Test, and even become a moral subject who can discern good from evil. This study attempts to provide some insights into the development of children's education from basic assumptions and construction goals of artificial intelligence, and to reflect on the causal model of artificial intelligence through children's education. (shrink)

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)

When a major engineering product failed, a failure investigation is often conducted to prevent similar failures in the future. In this dissertation, I propose an account of the epistemology and methodology of engineering failure investigations, based on a close examination of the documentations on five major plane crash investigations conducted by the National Transportation Safety Board (NTSB). -/- The dissertation is divided into three parts. The first part consists of the five case studies arranged in chronological order: the American Airlines (...) Flight 191 accident in 1979; the United Flight 232 accident in 1989; the United Flight 585 accident in 1991; the USAir Flight 427 accident in 1994; and the TWA Flight 800 accident in 1996. In each case study, I summarize the entire investigation process, focusing on articulating the questions that arise and the evidential reasoning that helped resolve each question. -/- The second part of the dissertation examines how the investigators infer causes of failure events. The type of causal inference used in failure investigations typically proceeds from effects to causes, hence it is called reverse causal inference. This is in contrast with forward causal inference, where researchers start with an intervention and infer the effects of that intervention. I identify three types of reverse causal inference in engineering failure investigations: feature dependence, additional outcomes, and process tracing. -/- The third part of the dissertation examines how the investigators construct narratives of failure events. At the end of each failure investigation, the investigators come up with a narrative detailing the sequence of events leading to the outcome. This part of the dissertation examines how the investigators construct such narratives and support them with evidence. I argue that both the construction and the justification of a narrative of a failure event depend on the question-and-answer process in the investigation, which I call the question dynamics of the investigation. I examine three main components of the question dynamics: the resolution of questions, the significance of questions, and the arising of questions. I conclude the dissertation with an account of the coherence of narratives, which is a measure of the evidential status of narratives. My account of coherence relies on the question dynamics, and it captures the intuitive idea that the pieces of a coherent narrative “fit together” very well. (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)

We argue that a modified version of Mill’s method of agreement can strongly confirm causal generalizations. This mode of causal inference implicates the explanatory virtues of mechanism, analogy, consilience, and simplicity, and we identify it as a species of Inference to the Best Explanation (IBE). Since rational causal inference provides normative guidance, IBE is not a heuristic for Bayesian rationality. We give it an objective Bayesian formalization, one that has no need of principles of (...) indifference and yields responses to the Voltaire objection, van Fraassen’s Bad Lot objection, and John Norton’s recent objection to IBE. (shrink)

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

Despite being a milestone in the history of statistical causal inference, Sewall Wright’s 1918 invention of path analysis did not receive much immediate attention from the statistical and scientific community. Through a careful historical analysis, this paper reveals some previously overlooked philosophical issues concerning the history of causal inference. Placing the invention of path analysis in a broader historical and intellectual context, I portray the scientific community’s initial lack of interest in the method as a natural (...) consequence of relevant scientific and philosophical conditions. In addition to Karl Pearson’s positivist refutation of causation, I contend that the acceptance of path analysis faced several other challenges, including the introduction of a new formalism, conceptual barriers to causal inference, and the lack of model-based statistical thinking. The presence of these challenges shows that the delayed progress in causal inference in the early twentieth century was inevitable. (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)

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

Frank Cabrera argues that informational explanatory virtues—specifically, mechanism, precision, and explanatory scope—cannot be confirmational virtues, since hypotheses that possess them must have a lower probability than less virtuous, entailed hypotheses. We argue against Cabrera’s characterization of confirmational virtue and for an alternative on which the informational virtues clearly are confirmational virtues. Our illustration of their confirmational virtuousness appeals to aspects of causal inference, suggesting that causal inference has a role for the explanatory virtues. We briefly explore (...) this possibility, delineating a path from Mill’s method of agreement to Inference to the Best Explanation (IBE). (shrink)

This chapter explores the idea that the need to establish common knowledge is one feature that makes social cognition stand apart in important ways from cognition in general. We develop this idea on the background of the claim that social cognition is nothing but a type of causal inference. We focus on autism as our test-case, and propose that a specific type of problem with common knowledge processing is implicated in challenges to social cognition in autism spectrum disorder (...) (ASD). This problem has to do with the individual’s assessment of the reliability of messages that are passed between people as common knowledge emerges. The proposal is developed on the background of our own empirical studies and outlines different ways common knowledge might be comprised. We discuss what these issues may tell us about ASD, about the relation between social and non-social cognition, about social objects, and about the dynamics of social networks. (shrink)

In the last several decades, a confluence of work in the social sciences, philosophy, statistics, and computer science has developed a theory of causal inference using directed graphs. This theory typically rests either explicitly or implicitly on two major assumptions.

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

This paper evaluates the claim that it is possible to use nature’s variation in conjunction with retention and selection on the one hand, and the absence of ultimate groundedness of hypotheses generated by the human mind as it knows on the other hand, to discard the ascription of ultimate certainty to the rationality of human conjectures in the cognitive realm. This leads to an evaluation of the further assumption that successful hypotheses with specific applications, in other words heuristics, seem to (...) have a firm footing because they were useful in another context. I argue that usefulness evaluated through adaptation misconstrues the search for truth, and that it is possible to generate talk of randomness by neglecting aspects of a system’s insertion into a larger situation. The framing of the problem in terms of the elimination of unfit hypotheses is found to be unsatisfying. It is suggested that theories exist in a dimension where they can be kept alive rather than dying as phenotypes do. The proposal that the subconscious could suggest random variations is found to be a category mistake. A final appeal to phenomenology shows that this proposal is orphan in the history of epistemology, not in virtue of its being a remarkable find, but rather because it is ill-conceived. (shrink)

Conspiracies are, by definition, a group activity; to conspire requires two or more people working together towards some end, typically in secret. Conspirators have intentions; this is borne out by the fact they want some end and are willing to engage in action to achieve. Of course, what these intentions are can be hard to fathom: historians have written a lot about the intentions of the assassins of Julius Caesar, for example; did they want to restore the Republic; was Marcus (...) Brutus lusting after power; was it an attempt to curb the ambitions of the plebeian class, who saw in Caesar someone not unlike themselves? If intentions can be hard to infer, who is a knowing conspirator and who is a dupe is almost as tricky to parse. Not all purported members of a conspiracy are conspirators, even if they aid and abet the conspiracy. Take, for example, the notorious lax engineer who the conspirators know will sign off on substandard building compliance measures, making it all the easier for them to plant their controlled demolitions. On some account of causal or even moral responsibility the lax engineer would then be responsible for the subsequent event, the one the conspirators desired. Yet it also seems clear that even if she is responsible in some sense for the culminating event of the conspiracy, it is clear that they did not conspire. Or, at least, we would like to think so, although should the authorities find out about the lax engineer’s involvement, they may well be considered part of the conspiracy, especially if the conspirators do their best to hide their own involvement or identities. (shrink)

How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be widely adopted in ordinary everyday life? Predominant frameworks on the psychology of concepts that shape work on stereotyping, bias, and machine learning have grim implications for the prospects of conceptual engineers: conceptual engineering efforts are ineffective in promoting certain social-conceptual changes. Specifically, since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely conceptual change won’t (...) be possible without corresponding world change. This tradition, however, tends to ignore that concepts don’t only encode statistical, but also causal information. Paying attention to this feature of concepts, I argue, shows that conceptual engineering is not only possible. There is an imperative to conceptually-engineer. (shrink)

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO (...) model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera. (shrink)

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by (...) a specific theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives. (shrink)

This paper takes a critical stance on Tallis’s separation of causation and agency. While his critique of the causal theory of action and the assumptions about causation underlying different versions of determinism, including the one based on neuroscience is right, his rejection of causation (of all sorts) has implausible consequences. Denying the link between action and causation amounts to overlooking the role action plays in causal inference and in the origin of causal concepts. I suggest that (...) a weaker version of Tallis’ claim, compatible with causation understood as agency, would work better. (shrink)

We humans are natural-born engineers. As such, we model after machines not only isolated, naturally occurring systems, but also the basic laws of physics, sharing with machines a local-evolution-of-state `grammar'. However, previous work by the author casts doubt upon this mechanistic paradigm, suggesting that it is to blame for the stubbornness of many open problems in physics. Simple experiments are therefore proposed to identify `non-machines'. In one experiment, `non mechanistic correlations' in the spirit of Bell are sought in a pair (...) of separated but previously coupled (macroscopic) chaotic systems. In another it is tested whether chaotic systems could fuzzily `remember their future' in the sense that a future binary perturbation applied to them could be inferred from their present behavior with probability$>1/2$. Chaotic systems are chosen as candidates because the long-time application of their (deterministic) mechanistic description is conceptually groundless: All scales are significant and mutually coupled in that regime, down to scales governed by QM, yet the quantum-classical transition remains inexplicable---if only due to the measurement problem. Similarly for non-deterministic chaotic systems, modeled as such purely for defying a short-time deterministic mechanistic description. Should a non mechanistic signature be found in such simple systems, the implications for science as a whole and life sciences in particular cannot be overstated. (shrink)

The authors examined cue competition effects in young children using the blicket detector paradigm, in which objects are placed either singly or in pairs on a novel machine and children must judge which objects have the causal power to make the machine work. Cue competition effects were found in a 5- to 6-year-old group but not in a 4-year-old group. Equivalent levels of forward and backward blocking were found in the former group. Children's counterfactual judgments were subsequently examined by (...) asking whether or not the machine would have gone off in the absence of I of 2 objects that had been placed on it as a pair. Cue competition effects were demonstrated only in 5- to 6-year-olds using this mode of assessing causal reasoning. (shrink)

This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the (...) mechanisms that might underlie their hypotheses. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms. (shrink)

Conceptual engineering is a philosophical method that aims to design and spread conceptual and linguistic devices to cause meaningful changes in the world. So far, however, conceptual engineers have struggled to successfully spread the conceptual and linguistic entities they have designed to their target communities. This paper argues that conceptual engineering is far more likely to succeed if it incorporates empirical data and empirical methods. Because the causal factors influencing successful propagation of linguistic or conceptual devices are as complicated (...) and interwoven as they are, proper empirical research will greatly boost the likelihood that propagation is successful. In arguing for the superiority of empirical conceptual engineering over armchair-based conceptual engineering, this paper proposes a framework for understanding the causal forces at play in propagation. This is a three-part framework between the label of a lexical item, the psychological states associated with the lexical item, and the worldly things associated with the lexical item. By understanding the way causal forces affecting propagation play out at these three levels, conceptual engineers can better conceptualize, study, and harness the different causal forces affecting the success of their conceptual engineering projects. (shrink)

A process of intelligence gathering begins when a user enters a query into the system. Several objects can match the result of a query with different degrees of relevance. Most systems estimate a numeric value about how well each object matches the query and classifies objects according to this value. Many researches have focused on practices of intelligence gathering. In knowledge engineering, knowledge gathering consists in fiding it from structured and unstructured sources in a way that must represent knowledge in (...) a way that facilitates inference. DOI: 10.13140/RG.2.2.32191.15527. (shrink)

Explanation is asymmetric: if A explains B, then B does not explain A. Tradition- ally, the asymmetry of explanation was thought to favor causal accounts of explanation over their rivals, such as those that take explanations to be inferences. In this paper, we develop a new inferential approach to explanation that outperforms causal approaches in accounting for the asymmetry of explanation.

How do mental states come to be about something other than their own operations, and thus to serve as ground for effective action? This papers argues that causation in the mental domain should be understood to function on principles of intelligibility (that is, on principles which make it perfectly intelligible for intentions to have a causal role in initiating behavior) rather than on principles of mechanism (that is, on principles which explain how causation works in the physical domain). The (...) paper considers Dharmakīrti’s kāryānumāna argument (that is, the argument that an inference is sound only when one infers from the effect to the cause and not vice versa), and proposes a naturalized account of reasons. On this account, careful scrutiny of the effect can provide a basis for ascertaining the unique causal totality that is its source, but only for reasoning that is context‐specific. (shrink)

This essay focuses on conceptual engineers who aim to improve other people's patterns of inference and attention by shaping their concepts. Such conceptual engineers sometimes engage in a form of epistemic paternalism that I call paternalistic cognitive engineering: instead of explicitly persuading, informing and educating others, the engineers non-consultatively rely on assumptions about the target agents’ cognitive systems to improve their belief forming. The target agents could reasonably regard such benevolent exercises of control as violating their sovereignty over their (...) own belief formation. This is a pro tanto reason against such engineering. In addition to the relevant projects of conceptual engineering, paternalistic cognitive engineering plausibly includes certain kinds of nudging and evidence suppression. I distinguish the sovereignty-based concern from other ethical worries about conceptual engineering and discuss how one might justify the relevant conceptual engineering projects despite the sovereignty-based reason against them. (shrink)

Davidson’s account of singular causal statements as expressing relations between events together with his views on event identity lead to inferences involving causal statements which many of his critics find counterintuitive. These are sometimes said to be avoided on Kim’s view of events, in terms of which this line of criticism is often formulated. It is argued that neither Davidson nor Kim offer a satisfactory account of events - an essential prerequisit for the relational theory - and an (...) account of singular causal statements in terms of a modal sentential connective is advocated in place of the relational view. Such an account suffices to block the counter-intuitive inferences without needing to resort to a theory of events. It is suggested that a theory of events might be built upon a connective account of singular causal statements, but no such theory is presented here. (shrink)

In the ‘Refutation of Idealism’ chapter of the first Critique, Kant argues that the conditions required for having certain kinds of mental episodes are sufficient to guarantee that there are ‘objects in space’ outside us. A perennially influential way of reading this compressed argument is as a kind of causal inference: in order for us to make justified judgements about the order of our inner states, those states must be caused by the successive states of objects in space (...) outside us. Here I consider the best recent versions of this reading, and argue that each suffers from apparently fatal flaws. -/- . (shrink)

Unlike conceptual analysis, conceptual engineering does not aim to identify the content that our current concepts do have, but the content which these concepts should have. For this method to show the results that its practitioners typically aim for, being able to change meanings seems to be a crucial presupposition. However, certain branches of semantic externalism raise doubts about whether this presupposition can be met. To the extent that meanings are determined by external factors such as causal histories or (...) microphysical structures, it seems that they cannot be changed intentionally. This paper gives an extended discussion of this ‘externalist challenge’. Pace Herman Cappelen’s recent take on this issue, it argues that the viability of conceptual engineering crucially depends on our ability to bring about meaning change. Furthermore, it argues that, contrary to first appearance, causal theories of reference do allow for a sufficient degree of meaning control. To this purpose, it argues that there is a sense of what is called ‘collective long-range control’, and that popular versions of the causal theory of reference imply that people have this kind of control over meanings. (shrink)

Many tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages. Despite several senses in which causal reasoning is indeed more complex—both expressively and inferentially—we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to purely probabilistic problems. Thus there is (...) no jump in computational complexity. Along the way we answer several open problems concerning the complexity of well-known probability logics, in particular demonstrating the ${\exists \mathbb {R}}$ -completeness of a polynomial probability calculus, as well as a seemingly much simpler system, the logic of comparative conditional probability. (shrink)

There are simple mechanical systems that elude causal representation. We describe one that cannot be represented in a single directed acyclic graph. Our case suggests limitations on the use of causal graphs for causal inference and makes salient the point that causal relations among variables depend upon details of causal setups, including values of variables.

I will argue that a person is causally responsible for believing what she does. Through inference, she can sustain and change her perspective on the world. When she draws an inference, she causes herself to keep or to change her take on things. In a literal sense, she makes up her own mind as to how things are. And, I will suggest, she can do this voluntarily. It is in part because she is causally responsible for believing what (...) she does that there are things that she ought to believe, and that what she believes can be to her credit or discredit. I won’t pursue these ethical matters here, but will focus instead on the metaphysics that underpin them. (shrink)

This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action (...) explanation has a modal strength like that of distinctively mathematical explanation, rather than that of causal explanation. Because it is stronger than causation, it can be treated as if it were merely causal in a causal model without thereby overextending the justification it can provide for inferences. This chapter introduces and demonstrate the usage of the Rationalization condition in causal modeling, where it is apt for the system(s) being modeled, and to provide the basics for incorporating R variables into systems of variables and R arrows into DAGs. Use of the Rationalization condition supplements causal analysis with action analysis where it is apt. (shrink)

This thesis brings together two concerns. The first is the nature of inference—what it is to infer—where inference is understood as a distinctive kind of conscious and self-conscious occurrence. The second concern is the possibility of doxastic agency. To be capable of doxastic agency is to be such that one is capable of directly exercising agency over one’s beliefs. It is to be capable of exercising agency over one’s beliefs in a way which does not amount to mere (...) self-manipulation. Subjects who can exercise doxastic agency can settle questions for themselves. A challenge to the possibility of doxastic agency stems from the fact that we cannot believe or come to believe “at will”, where this in turn seems to be so because belief “aims at truth”. It must be explained how we are capable of doxastic agency despite that we cannot believe or come to believe at will. On the orthodox ‘causalist’ conception of inference for an inference to occur is for one act of acceptance to cause another in some specifiable “right way”. This conception of inference prevents its advocates from adequately seeing how reasoning could be a means to exercise doxastic agency, as it is natural to think it is. Suppose, for instance, that one reasons and concludes by inferring where one’s inference yields belief in what one infers. Such an inference cannot be performed at will. We cannot infer at will when inference yields belief any more than we can believe or come to believe at will. When it comes to understanding the extent to which one could be exercising agency in such a case the causalist conception of inference suggests that we must look to the causal history of one’s concluding act of acceptance, the nature of the act’s being determined by the way in which it is caused. What results is a picture on which such reasoning as a whole cannot be action. We are at best capable of actions of a kind which lead causally to belief fixation through “mental ballistics”. The causalist account of inference, I argue, is in fact either inadequate or unmotivated. It either fails to accommodate the self-consciousness of inference or is not best placed to play the very explanatory role which it is put forward to play. On the alternative I develop when one infers one’s inference is the conscious event which is one’s act of accepting that which one is inferring. The act’s being an inference is determined, not by the way it is caused, but by the self-knowledge which it constitutively involves. This corrected understanding of inference renders the move from the challenge to the possibility of doxastic agency to the above ballistics picture no longer tempting. It also yields an account of how we are capable of exercising doxastic agency by reasoning despite being unable to believe or come to believe at will. In order to see how such reasoning could amount to the exercise of doxastic agency it needs to be conceived of appropriately. I suggest that paradigm reasoning which potentially amounts the exercise of doxastic agency ought to be conceived of as primarily epistemic agency—agency the aim of which is knowledge. With inference conceived as suggested, I argue, it can be seen how to engage in such reasoning can just be to successfully exercise such agency. (shrink)

Peirce’s pragmatic maxim is closely related to his conception of abduction. The acquisition of the actual effect required by the method of scientific reasoning expressed by Peirce’s maxim must be accomplished by resorting to abductive logic. Abductive logic starts from a surprising fact, derives a hypothetical explanation about that fact, and finally arrives at the possibility that the hypothesis is true. This is the process of abductive reasoning, as provided by Peirce, which is distinct from induction and deduction and generates (...) explanatory views. Peirce opposed a unified and unchangeable concept of causality. He used different interpretations of causality to illustrate the considerable differences in people’s understanding of cause and effect in different periods. The concept of pragmatism, as developed from the pragmatic maxim to abduction and then to scientific inference to the best explanation, is precisely what Peirce initially proposed, and inference to the best explanation is the starting point and the final result of the pragmatic maxim. (shrink)

The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in the semantic view of (...) theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science. (shrink)

This chapter presents a typology of the different kinds of inductive inferences we can draw from our evidence, based on the explanatory relationship between evidence and conclusion. Drawing on the literature on graphical models of explanation, I divide inductive inferences into (a) downwards inferences, which proceed from cause to effect, (b) upwards inferences, which proceed from effect to cause, and (c) sideways inferences, which proceed first from effect to cause and then from that cause to an additional effect. I further (...) distinguish between direct and indirect forms of downwards and upwards inferences. I then show how we can subsume canonical forms of inductive inference mentioned in the literature, such as inference to the best explanation, enumerative induction, and analogical inference, under this typology. Along the way, I explore connections with probability and confirmation, epistemic defeat, the relation between abduction and enumerative induction, the compatibility of IBE and Bayesianism, and theories of epistemic justification. (shrink)

This article provides a conceptual account of causal understanding by connecting current psychological research on time and causality with philosophical debates on the causal asymmetry. I argue that causal relations are viewed as asymmetric because they are understood in temporal terms. I investigate evidence from causal learning and reasoning in both children and adults: causal perception, the temporal priority principle, and the use of temporal cues for causal inference. While this account does not (...) suffice for correct inferences of causal structure, I show it to serve as a preliminary understanding of causal concepts as asymmetric, that later incorporates other types of evidence. This approach supplies causal models with an asymmetric concept of causation that underlies hypotheses about causal structure, as I will illustrate from the framework of the knowledge-based causal induction model. I further argue for an integrating perspective, showing how the understanding of causes as preceding their effects underlies both psychological models and philosophical debates over time and the causal asymmetry, particularly regarding problem cases such as simultaneous causation or backwards causation, and the conceptual connection between causation and action. (shrink)

Legal concepts can sometimes be unclear, leading to disagreements concerning their contents and inconsistencies in their application. At other times, the legal application of a concept can be entirely clear, sharp, and free of confusions, yet conflict with the ways in which ordinary people or other relevant stakeholders think about the concept. The aim of this chapter is to investigate the role of experimental jurisprudence in articulating and, ultimately, dealing with competing conceptual inferences either within a specific domain (e.g., legal (...) practice) or between, for example, ordinary people and legal practitioners. Although this chapter affirms the widespread assumption that experimental jurisprudence cannot, in and of itself, tell us which concepts should be applied at law, it highlights some of the contributions that experimental jurisprudence can, in principle, make to normative projects that seek to prescribe, reform, or otherwise engineer legal concepts. Thus, there is more that experimental jurisprudence can normatively offer than has usually been claimed. (shrink)

Formalizing commonsense knowledge for reasoning about time has long been a central issue in AI. It has been recognized that the existing formalisms do not provide satisfactory solutions to some fundamental problems, viz. the frame problem. Moreover, it has turned out that the inferences drawn do not always coincide with those one had intended when one wrote the axioms. These issues call for a well-defined formalism and useful computational utilities for reasoning about time and change. Yoav Shoham of Stanford University (...) introduced in his 1986 Yale doctoral thesis an appealing temporal nonmonotonic logic and identified a class of theories, causal theories, which have computationally simple model-theoretic properties. This paper is a study towards building upon Shoham's work on causal theories. We concentrate on improving computational aspects of causal theories while preserving their model-theoretic properties. (shrink)

What is an inference and when is an inference deductive rather than inductive, abductive, etc. The goal of this paper is precisely to determine what is that we, humans, do when we engage in deduction, i.e., whether there is something that satisfies both our pre-theoretical intuitions and theoretical presuppositions about deduction, as a cognitive process. The paper is structured in two parts: the first one deals with the issue of what is an inference. There, I will defend (...) the hypothesis that an inference is a causal process where an initial cognitive state causes another in such a way that the causal translation depends essentially on the content of the involved states. In other words, in an inference, a mental state A causes another B, partially because they have the content they have. Based on the conclusion from the first part, I will develop an account of deductive inference in the second part of the article. In order to achieve this, I will start by setting the stage and developing four constraints that an adequate account of deductive inference ought to satisfy. Then, I will present six different hypotheses that one might propose to characterize inferences of this kind. I have divided the proposals in two groups, the first one – which I will call the “logical proposals” – assume that whether an inference is a deduction or not depends exclusively on the content of the mental states involved in the inference, while the second – which I will call the “cognitive proposals” – reject the aforementioned assumption and instead recognize that extra cognitive information is required to determine whether an inference is a deduction or not. I will evaluate each proposal in turn, according to whether they satisfy the constraints introduced at the beginning of this second part. As a result of such evaluation, I will conclude that only one of the cognitive proposals actually satisfies the four constraints and therefore should be considered to give an adequate characterization of deductive inference. (shrink)

I discuss two categories of causal relationships: primitive causal interactions of the sort characterized by Phil Dowe and the more general manipulable causal relationships as defined by James Woodward. All primitive causal interactions are manipulable causal relationships, but there are manipulable causal relationships that are not primitive causal interactions. I’ll call the latter constructed causal relationships, and I’ll argue that constructed causal relationships serve as a foundation for both computing and complex (...) systems. -/- Perhaps even more interesting are autonomous causal relationships. These are constructed causal relationships in which the causal mechanism resides primarily in the effect. A typical example is a software execution engine. Software execution engines are on the effect side of a cause-effect relationship in which software is the cause and the behavior of the execution engine is the effect. The mechanism responsible for that causal relationship resides in the execution engine. (shrink)

In this BA dissertation, I deploy examples of non-causal explanations of physical phenomena as evidence against the view that causal models of explanation can fully account for explanatory practices in science. I begin by discussing the problems faced by Hempel’s models and the causal models built to replace them. I then offer three everyday examples of non-causal explanation, citing sticks, pilots and apples. I suggest a general form for such explanations, under which they can be phrased (...) as inductive-statistical arguments incorporating plausible assumptions. I then show the applicability of this form to explanatory practices in thermal physics. I explore the possibility that population genetics provides a similar form of explanation, and offer a novel defence of the statistical interpretation of population genetics proposed by Matthen and Ariew (2002). I close with remarks concerning how, when faced with competing causal and non-causal explanations of the same phenomenon, we can perform an Inference to the Best Explanation. (shrink)

Engineering failure investigations seek to reconstruct the actual causes of major engineering failures. The investigators need to establish the existence of certain past events and the actual causal relationships that these events bear to the failures in question. In this paper, I examine one method for reconstructing the actual causes of failure events, which I call "feature dependence". The basic idea of feature dependence is that some features of an event are informative about the features of its causes; therefore, (...) the investigators can use the features of a known failure event to reconstruct details of its causes. I make explicit the structure of feature dependence and the evidential basis of its key premises, and show how feature dependence works in the investigation of the American Airlines Flight 191 accident. (shrink)

In Lehrer’s case of the superstitious lawyer, a lawyer possesses conclusive evidence for his client’s innocence, and he appreciates that the evidence is conclusive, but the evidence is causally inert with respect to his belief in his client’s innocence. This case has divided epistemologists ever since Lehrer originally proposed it in his argument against causal analyses of knowledge. Some have taken the claim that the lawyer bases his belief on the evidence as a data point for our theories to (...) accommodate, while others have denied that the lawyer has knowledge, or that he bases his belief on the evidence. In this paper, we move the dialectic forward by way of arguing that the superstitious lawyer genuinely infers his client’s innocence from the evidence. To show that the lawyer’s inference is genuine, we argue in defense of a version of a doxastic construal of the ‘taking’ condition on inference. We also provide a pared-down superstitious lawyer-style case, which displays the key features of the original case without including its complicated and distracting features. But interestingly, although we argue that the lawyer’s belief is based on his good evidence, and is also plausibly doxastically justified, we do not argue that the lawyer knows that his client is innocent. (shrink)

According to a common objection to epistemological naturalism, no empirical, scientific theory of knowledge can be normative in the way epistemological theories need to be. In response, such naturalists as W.V. Quine have claimed naturalized epistemology can be normative by emulating engineering disciplines and addressing the relations of causal efficacy between our cognitive means and ends. This paper evaluates that "engineering reply" and finds it a mixed success. Based on consideration of what it might mean to call a theory (...) "normative," seven versions of the normativity objection to epistemological naturalism are formulated. The engineering reply alone is sufficient to answer only the four least sophisticated versions. To answer the others, naturalists must draw on more resources than their engineering reply alone provides. (shrink)

We contend that it is possible to argue reasonably for and against arguments from classifications and definitions, provided they are seen as defeasible (subject to exceptions and critical questioning). Arguments from classification of the most common sorts are shown to be based on defeasible reasoning of various kinds represented by patterns of logical reasoning called defeasible argumentation schemes. We show how such schemes can be identified with heuristics, or short-cut solutions to a problem. We examine a variety of arguments of (...) this sort, including argument from abductive classification, argument from causal classification, argument from analogy-based classification and arguments from classification based on generalizations. (shrink)

I defend the view, hardly original with me, that there is no evidence, deductive or non-deductive, for any of our causal beliefs, that does not already assume that there are some causal connections, and hence that there is no way in which experience on its own, or with causalität-free principles, can support the structure of out causal knowledge. The deductive case is perhaps obvious. In the case of non-deductive arguments, I consider how experience of constant conjunctions, together (...) with the employment of an inference to the best explanation, might lead to a causal conclusion. But I argue that such an inference would need as a premiss that the causal explanation of the constant conjunction is the best explanation of that evidence, and ‘best’ in this context requires the thought that the causal hypothesis best fits with our existing causal beliefs. (shrink)

The paper argues that on three out of eight possible hypotheses about the EPR experiment we can construct novel and realistic decision problems on which (a) Causal Decision Theory and Evidential Decision Theory conflict (b) Causal Decision Theory and the EPR statistics conflict. We infer that anyone who fully accepts any of these three hypotheses has strong reasons to reject Causal Decision Theory. Finally, we extend the original construction to show that anyone who gives any of the (...) three hypotheses any non-zero credence has strong reasons to reject Causal Decision Theory. However, we concede that no version of the Many Worlds Interpretation (Vaidman, in Zalta, E.N. (ed.), Stanford Encyclopaedia of Philosophy 2014) gives rise to the conflicts that we point out. (shrink)