Keeping Score for Causal Claims: Causal Contextualism applied to a Medical Case

This article investigates how Causal Contextualism applies in a medical context. It is shown how the correct interpretation of some medical causal claims depends on relevant alternatives and then argued that these relevant alternatives are determined by standards of practice and practical limitations (of equipment, personnel, expertise, cost), amongst other factors. Causal Contextualism has recently been defended by a number of philosophers; however details of the relevant factors determining content in different contexts have been lacking. It seems to me that establishing such details of Causal Contextualism goes a long way towards making the view plausible, and is also necessary for discovering some important consequences of Causal Contextualism.     

Causal modeling with the TETRAD program

Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. Finally, we illustrate TETRAD's effectiveness by applying it to a multiple indicator model of Political and Industrial development. A pilot version of the TETRAD program is described in this paper. The current version is described in our forthcoming Discovering Causal Structure: Artificial Intelligence for Statistical Modeling.     

Recruitment modeling applied to longitudinal studies of group differences in intelligence

In longitudinal studies data is collected in a series of waves. Each wave after the first suffers from attrition. Therefore it can be difficult to discriminate between changes in sample parameters due to a longitudinal process (e.g. ageing) and changes due to attrition. The problem is particularly vexing if one of the purposes is to compare differences between two or more groups over time, because there may be differential attrition across groups. We refer to this as a recruitment problem, because the sample studied in any wave after the first can be thought of as having been recruited from a preceding wave. We present a mathematical model of the recruitment process. We then apply it to two large longitudinal studies, the British Cohort Survey 1970 and the National Child Development Study (1958), to demonstrate how we can identify effects expected from recruitment alone. As an illustration we show that differential recruitment would predict increasing differences between estimates of men's and women's intelligence as the studied sample ages.     

Multilevel modeling of two cyclical processes: extending differential structural equation modeling to nonlinear coupled systems

The authors present a dynamical multilevel model that captures changes over time in the bidirectional, potentially asymmetric influence of 2 cyclical processes. S. M. Boker and J. Graham's (1998) differential structural equation modeling approach was expanded to the case of a nonlinear coupled oscillator that is common in bimanual coordination studies in which participants swing hand-held pendulums but is also applicable to social systems in general. The authors' nonlinear coupled oscillator model decomposed the fluctuations into a competitive component, unique to each individual variable, and a cooperative component that captured bidirectional influence. The authors' model also generated an index of the symmetry/asymmetry of bidirectional influence. Together, the models are useful quantitative tools for the study of interacting, changing processes.     

Effects of Causal Structure on Decisions About Where to Intervene on Causal Systems

We investigated how people design interventions to affect the outcomes of causal systems. We propose that the abstract structural properties of a causal system, in addition to people's content and mechanism knowledge, influence decisions about how to intervene. In Experiment 1, participants preferred to intervene at specific locations (immediate causes, root causes) in a causal chain regardless of which content variables occupied those positions. In Experiment 2, participants were more likely to intervene on root causes versus immediate causes when they were presented with a long‐term goal versus a short‐term goal. These results show that the structural properties of a causal system can guide the design of interventions.     

Cognitive simulation modeling on the VAX-11

The ability of the VAX-11 to serve as both a real-time and a large-memory-space machine is described. The issue of how to do cognitive simulation work on the VAX-11 is addressed.     

Risk behaviour and mental workload: Multimodal assessment techniques applied to motorbike riding simulation

We present data from an ongoing research project on the cognitive, emotional and neuropsychological basis of risk behaviour. The main aim of the project is to build a model of risk behaviour so that if we know certain cognitive, behavioural and emotional variables, we will be able to predict decisions made in the face of uncertainty and risk, with the final goal of designing programs for evaluating, preventing and controlling risk behaviour. The objective of the present study was to look for individual differences in hazard perception during a static riding simulation and their relationship with mental workload. We used a multidimensional methodology, including behavioural, subjective and physiological data. The behavioural measures were obtained in a static riding simulation during eight hazard situations. We evaluated whether eye activity measures correlated with cognitive workload and different types of risky behaviours. Eye movement parameters were measured using a video-based eye tracking system. We found that risk-prone individuals showed specific patterns of risky behaviours and that peak of saccadic velocity and subjective mental workload indexes were both reliable indicators of risk proneness. Mental workload was higher for participants showing attitudes to risk behaviours probably because of a lack of conscious awareness of specific cues indicating dangerous scenarios.     

Causal modeling of relations among learning history, anxiety sensitivity, and panic attacks

We used structural equation modeling (SEM) to test the hypothesis that childhood instrumental and vicarious learning experiences influence frequency of panic attacks in young adulthood both directly, and indirectly through their effects on anxiety sensitivity (AS). A total of 478 university students participated in a retrospective assessment of their childhood learning experiences for arousal-reactive sensations (e.g., nausea, racing heart, shortness of breath, dizziness) and arousal-non-reactive sensations (i.e., colds, aches and pains, and rashes). SEM revealed that learning history for arousal-reactive somatic symptoms directly influenced both AS levels and panic frequency; AS directly influenced panic frequency; and learning history for arousal-non-reactive symptoms directly influenced AS but did not directly influence panic frequency. These results are consistent with the findings of previous retrospective studies on the learning history origins of AS and panic attacks, and provide the first empirical evidence of a partial mediation effect of AS in explaining the relation between childhood learning experiences and panic attacks in young adulthood. Implications for understanding the etiology of panic disorder are discussed.     

Mathematical modeling of movement between residential placements: A systems analytic approach to understanding systems of care

Although the concept of a system of care for children and adolescents with a severe emotional disturbance has become core to treatment philosophy, very little research has focused on systems of care as systems. This article attempts to contribute to the development of a systems research paradigm in children's mental health as well as to add to the substantive knowledge of utilization of and movement between residential options in a statewide multiagency service delivery system. Information on residential placement gathered at six month intervals over a three year period is used to develop a mathematical model of the system of care. This model can help administrators to predict future needs for residential care, and to project probable outcomes of alternative strategies for meeting the needs of these children and adolescents.     

The Causal Relevance of Content to Computation

Many philosophers worry that the classical computational theory of mind (CTM) engenders epiphenomenalism. Building on Block’s (1990) discussion, I formulate a particularly troubling version of this worry. I then present a novel solution to CTM’s epiphenomenalist conundrum. I develop my solution within an interventionist theory of causal relevance. My solution departs substantially from orthodox versions of CTM. In particular, I reject the widespread picture of digital computation as formal syntactic manipulation. 1     

Causal Binding of Actions to Their Effects

ABSTRACT— According to widely held views in cognitive science harking back to David Hume, causality cannot be perceived directly, but instead is inferred from patterns of sensory experience, and the quality of these inferences is determined by perceivable quantities such as contingency and contiguity. We report results that suggest a reversal of Hume's conjecture: People's sense of time is warped by the experience of causality. In a stimulus-anticipation task, participants' response behavior reflected a shortened experience of time in the case of target stimuli participants themselves had generated, relative to equidistant, equally predictable stimuli they had not caused. These findings suggest that causality in the mind leads to temporal binding of cause and effect, and extend and generalize beyond earlier claims of intentional binding between action and outcome.     

Causal Exclusion and Causal Bayes Nets

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.     

Assessing the simulation of chaotic systems

Three commercially available software packages—BrainMaker, MicroSaint, and Extend—that simulate the function and operation of neural networks were compared on 11 dimensions. Also, a task-oriented analysis was done on the ability of the programs to use a neural network to output a chaotic array generated by the logistic equation. There were differences in the amount of operator overhead among the programs, but the differences in the simulation performance were not considered to be appreciable.     

Observational assessment systems use for basic and applied research

Three observational assessment systems, the Time-Sample Behavioral Checklist, the Staff-Resident Interaction Chronograph, and the Clinical Frequencies Recording System, are shown to have remarkable utility for a wide range of basic and applied research questions. The assessment systems have already been employed to measure independent and dependent variables in a number of treatment outcome and staff training studies and as measures in more basic studies of environmental history effects for chronic mental patients. Current and planned investigations employing the observational measures are outlined. These include psychometric studies of the instruments themselves, investigations of various parameters of ongoing treatment programs, and empirical validations of proposed nosological diagnostic schemas. Finally, several metamethodological issues germaine to observational assessment, in general, are noted, including the observation vs. interpretation debate and the role of precise observational measurement in applied research contexts.The research reported in this article was supported, in part, by Public Health Service Grants MH-15553 and MH-25464 from the National Institute of Mental Health, and by grants from The Joyce Foundation, Illinois Department of Mental Health and Developmental Disabilities, and the Purdue Research Foundation.Based on a paper presented at the 87th Annual Meeting of the American Psychological Association, New York City, September 1979, as part of a symposium New Assessment systems for residential treatment, management, research, and evaluation.     

Children Use Temporal Cues to Learn Causal Directionality

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real‐world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.     

Correction to: Causal Sufficiency and Actual Causation

Journal of Philosophical Logic - A Correction to this paper has been published: https://doi.org/10.1007/s10992-021-09632-6     

Causal Closure,Causal Exclusion,and Supervenience Physicalism

This article considers the recent defense of the supervenience approach to physicalism due to Jaegwon Kim. Kim argues that supervenience supports physical causal closure, and that causal closure supports physicalism – indeed, a kind of reductive physicalism – and thus that supervenience suffices for physicalism. After laying out Kim's argument, I ask whether its success would truly vindicate the role of supervenience in defining physicalist positions. I argue that it would not, and that insofar as Kim's defense of supervenience physicalism succeeds, it does so by showing that supervenience physicalism is not a unique, nonredundant way to be a physicalist.