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PubMed2006

The psychophysical law of speed estimation in Michotte's causal events.

Observers saw an event in which a computer-animated square moved up to and made contact with another, which after a short delay moved off, its motion appearing to be caused by launch by the first square. Observers chose whether the second (launched) square was faster in this causal event than when presented following a long delay (non-causal event). The speed of the second object in causal events was overestimated for a wide range of speeds of the first object (launcher), but accurately assessed in non-causal events. Experiments 2 and 3 showed that overestimation occurred also in other causal displays in which the trajectories were overlapping, successive, spatially separated or inverted but did not occurred with consecutive speeds that did not produce causal percepts. We also found that if the first object in a causal event was faster, then Weber's law holds and overestimation of the launched object speed was proportional to the speed of the launcher. In contrast, if the second object was faster, overestimation was constant, i.e. independent of the launcher. We propose that the particular speed integration of causal display results in overestimation and that the way overestimation depends on V1 phenomenally affects the attribution of the source of V2 motion: either in V1 (in launching) or in V2 (in triggering).

Discrimination, Psychological↗

PubMed2001

Estimating causal strength: the role of structural knowledge and processing effort.

The strength of causal relations typically must be inferred on the basis of statistical relations between observable events. This article focuses on the problem that there are multiple ways of extracting statistical information from a set of events. In causal structures involving a potential cause, an effect and a third related event, the assumed causal role of this third event crucially determines whether it is appropriate to control for this event when making causal assessments between the potential cause and the effect. Three experiments show that prior assumptions about the causal roles of the learning events affect the way contingencies are assessed with otherwise identical learning input. However, prior assumptions about causal roles is only one factor influencing contingency estimation. The experiments also demonstrate that processing effort affects the way statistical information is processed. These findings provide further evidence for the interaction between bottom-up and top-down influences in the acquisition of causal knowledge. They show that, apart from covariation information or knowledge about mechanisms, abstract assumptions about causal structures also may affect the learning process.

Adult↗

PubMed1998

Why are different features central for natural kinds and artifacts?: the role of causal status in determining feature centrality.

Ahn and Lassaline [Ahn, W., Lassaline, M.E., 1995. Causal structure in categorization. Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society, Pittsburgh, PA, pp. 521-526] recently proposed a causal status hypothesis which states that features that play a causal role in a relational structure are more central than their effects. This hypothesis can account for previous research demonstrating that compositional features are generally important for natural kinds but functional features are generally important for artifacts. The causal status hypothesis explains this category-feature interaction effect in terms of differences in the causal status of compositional and functional features between natural kinds and artifacts. Experiments 1 and 2 examined real-life categories used in previous studies, and found positive correlations between the causal status of the features and their centrality across natural and artifactual kinds. Experiments 3 and 4 manipulated the causal status of compositional and functional features in artificial categories, and showed that it was causal status rather than the interaction between the type of feature and the type of category per se that accounted for feature centrality. The implications of these results on the distinctions between natural kinds and artifacts are discussed.

Adult↗

PubMed2005

The power PC theory and causal powers: comment on Cheng (1997) and Novick and Cheng (2004).

It has been claimed that the power PC theory reconciles regularity and power theories of causal judgment by showing how contingency information is used for inferences about unobservable causal powers. Under the causal powers theory causal relations are understood as generative relations in which a causal power of one thing acts on a liability of another thing under some releasing condition. These 3 causal roles are implicit or explicit in all causal interpretations. The power PC theory therefore fails to reconcile power theories and regularity theories because it has a fundamentally different definition of power and does not accommodate the tripartite causal role distinction. Implications of this distinction are drawn out.

Cognition↗

PubMed1999

Applications of the causality condition to one-dimensional acoustic reflection problems.

The causality condition is examined as a means of determining frequency-domain information about a submerged object from a partial knowledge of its acoustic reflection characteristics. A one-dimensional problem is considered in which an acoustic wave reflects from an object that is described by the impedance it presents to the fluid. Two new applications of the causality condition to the frequency-domain analysis of this problem are investigated and illustrated by numerical examples. In each application, the causality condition is used to find the object's complex impedance from a knowledge of the reflected wave's magnitude. The first application is to experimental studies where one desires a knowledge of an object's complex impedance but practical limitations only allow a measurement of the reflected wave amplitude. Analysis shows that the causality condition may be used to determine the phase of the reflected wave, and hence the object's impedance, if the reflection coefficient is minimum phase. When this is true, examples suggest that the phase of the reflection coefficient may be accurately determined from the causality condition even in the presence of noise and band-limited data. The second application is to design situations, where one wishes to create an object that reflects sound with a specified frequency-dependent magnitude. The causality condition may aid the designer by providing a knowledge of all causal object impedances that produce the same reflection coefficient magnitude. A numerical example is presented in which a variety of causal object impedances produce the same reflection coefficient magnitude over an infinite frequency range.

Acoustics↗

PubMed2006

Getting causal considerations back on the right track.

In their commentary on my paper Phillips and Goodman suggested that counterfactual causality and considerations on causality like those by Bradford Hill are only "guideposts on the road to common sense". I argue that if common sense is understood to mean views that the vast majority of researchers share, Hill's considerations did not lead to common sense in the past--precisely because they are so controversial. If common sense is taken to mean beliefs that are true, then Hill's considerations can only lead to common sense in the simple and well-understood causal systems they apply to. Counterfactuals, however, are largely common sense in the latter meaning.I suggest that the road of scientific endeavour should lead epidemiologic research toward sound strategies that equip researchers with skills to separate causal from non-causal associations with minimal error probabilities. This is undeniably the right direction and the one counterfactual causality leads to. Hill's considerations are merely heuristics with which epidemiologists may or may not find this direction, and they are likely to fail in complex landscapes (causal systems). In such environments, one might easily lose orientation without further aids (e.g., defendable assumptions on biases). Counterfactual causality tells us when and how to apply these heuristics.

Comment↗

PubMed2005

Competence and performance in causal learning.

The dominant theoretical approach to causal learning postulates the acquisition of associative weights between cues and outcomes. This reduction of causal induction to associative learning implies that learners are insensitive to important characteristics of causality, such as the inherent directionality between causes and effects. An ongoing debate centers on the question of whether causal learning is sensitive to causal directionality (as is postulated by causal-model theory) or whether it neglects this important feature of the physical world (as implied by associationist theories). Three experiments using different cue competition paradigms are reported that demonstrate the competence of human learners to differentiate between predictive and diagnostic learning. However, the experiments also show that this competence displays itself best in learning situations with few processing demands and with convincingly conveyed causal structures. The study provides evidence for the necessity to distinguish between competence and performance in causal learning.

Adult↗

PubMed1975

The chemotherapy of rodent malaria, XXIII Causal prophylaxis, part II: Practical experience with Plasmodium yoelii nigeriensis in drug screening.

Data are presented on the causal prophylactic action of about 100 compounds of various types against Plasmodium yoelii nigeriensis N67 in mice. Examples are given to show how action against pre-erythrocytic schizonts may be differentiated from action on emerging erythrocytic stages. In a series of 35 8-aminoquinolines, all but 10 showed definite causal prophylactic activity at tolerated doses. The data permit the compounds to be ranked in order of activity, and many are shown to be more active in this test system than primaquine. Marked causal prophylactic activity is displayed by a variety of quinone structures, several of which show a significant residual action on blood stages. A high level of activity is found in dihydrofolate reductase inhibitors within several chemical classes. Rorguanil is more effective as a causal prophylactic than a blood schizontocide in the mouse as in man. Sulphonamides and sulphones are also effective in this system. The active levels are influenced by the content of PABA in the diet of the hosts. Causal prophylactic action has been detected in a number of experimental compounds including some antibiotics (such as tetracycline and clindamycin). The pyrocatechol RC 12 shows only slight activity at the maximum tolerated dose. Chloroquine, mepacrine, quinine, quinolinemethanols and phenanthrenemethanols are inactive as causal prophylactics. It is concluded that a rodent malaria-mouse model does provide a relatively simple model for the screening of drugs for causal prophylaxis, and the data so obtained are of relevance to the detection of causal prophylactics against human malaria.

Amidines↗

PubMed1992

Causal propositions in clinical research and practice.

The concept of causation is central to clinical research and practice. The health science literature on causality, largely contributed by epidemiologists, has examined the population-based question of whether an exposure can cause a given health outcome. Most of this literature has focused on criteria for assessing causality, rather than attempting to define it. Moreover, the population-based approach is rather distant from the individual persons in whom causes must act, which has led to different perspectives on causality among epidemiologists and health policy markers, on the one hand, and clinical practitioners and the lay public, on the other. We attempt to bridge the gap between these perspectives by defining three probabilistic causal propositions based on the locus (individual vs population) and time frame (past vs future outcome) to which they refer, beginning with the individual in whom a health outcome has already occurred ("retrodictive" causal propositions, i.e. It Did) and proceeding to "potential" causal propositions (It Can) for populations and "predictive" causal propositions (It Will) for individuals or populations. We conclude by showing how attention to these distinctions may help avoid common pitfalls that can impair clinical or public health decision-making.

Causality↗

PubMed2006

Causal inference in primary open angle glaucoma: specific discussion on intraocular pressure.

OBJECTIVES: As the first part of a comprehensive review of the concept of causal inference in epidemiology, this article explains how causal inference is established in epidemiology and applies these methods to evaluate the evidence in favor or against the causal association between intraocular pressure (IOP) and primary open angle glaucoma (POAG) as an example. METHODS: After an introduction to causal inference in epidemiology and general guidelines for assessment of the causal relationship, the association between IOP and POAG will be evaluated in the context of these guidelines and the categories suggested by the Surgeon General's report. RESULTS: The literature indicates a consistent strong association between IOP and POAG and there is no strong evidence against temporal precedence. The association is biologically plausible, coherent with scientific principles, and has noticeable biological gradient. However, it seems that IOP is not specific for POAG and vice versa. CONCLUSION: Despite the absence of specificity, we conclude that the evidence is sufficient to infer a causal relationship between IOP and POAG. In sum, based on the literature and our current knowledge of glaucoma, currently most competing causes are not strong enough to confront the causal role of IOP.

Causality↗

PubMed1997

On the use of causal criteria.

BACKGROUND: Two recent accounts of the use of causal criteria make opposite claims: that criteria should be used more often to avoid bias in assessments of weak associations and, in direct contrast, that criteria are scientifically invalid. METHODS: A recent review of the current practice of causal inference in epidemiology, as well as some more theoretical concerns, reveals errors in the two claims. RESULTS: In practice, epidemiologists often use the criteria of consistency, strength, dose-response, and biological plausibility, but not often temporality, when judging weak associations. These criteria are used for causal assessments as well as for making public health recommendations. In theory, causal criteria can be used to either refute or predict causal effects. CONCLUSION: Research on causal inference methodology should be encouraged, including research on underlying theory, methodology, and additional systematic descriptions of how causal inference is practised. Specific research questions include: to what extent can consensus be achieved on definitions and accompanying rules of inference for criteria, the relationship of meta-analysis to the criterion of consistency, and the interrelationships of criteria such as consistency, strength of association, and biological plausibility.

Causality↗

PubMed1990

Epigenesis theory: a mathematical model relating causal concepts of pathogenesis in individuals to disease patterns in populations.

A mathematical modeling approach called epigenesis theory is presented which relates three aspects of pathogenesis to the population distribution of disease. The three aspects of pathogenesis involve how two or more measured variables interact. They are 1) whether the measured variables are related to the same causal action, 2) whether there is only one pathogenic process leading to disease, and 3) whether the measured variables contribute to the same pathogenic process. Epigenesis theory defines the following multivariable relations between two disease causes: 1) "Complementary" causes contribute different causal actions to the sole pathogenic process leading to disease. They have multiplicative relations. 2) "Separate process" causes contribute different causal actions to different pathogenic processes. They have the relations of simple independent action which are slightly less than additive. 3) "Intermediate" causes contribute different causal actions to the same pathogenic process in the presence of additional pathogenic processes where at most one of them may also participate. They have relations somewhere between multiplicative and simple independent actions. 4) "Cooperative-competitive" causes share the same causal action and act within the same pathogenic process. Their relations can change from greater than multiplicative to less than simple independent action at increasing dichotomization points of the measured variables. Epigenesis theory unifies the sufficient-component causes model and the simple independent action model and exceeds either model in the range of observations it can explain. It is most useful given directly causal measured variables and specific disease outcomes, but it will assist in etiologic investigations of nonspecific outcomes in which new disease classifications are proposed. While it is less useful given surveillance-type variables such as age or sex or outcomes resulting from numerous pathogenic processes such as death, it gains utility as more causal variables are entered into an analysis and as more cut points of continuous complementary, independent, or intermediate variables are distinguished.

Adult↗

PubMed1993

Causal attributions in the explanation of alcohol-related accidents.

This paper discusses issues related to the cognitive and communicational activity of ascribing a causal role to alcohol use in accidents. It is argued that in addition to the empirical relationships to be explained causal attribution is limited by two other types of empirical contingencies: the cognitive processing of information available for causal attribution, and the representation of this information in language (encoding and decoding as part of communication). Only the latter two types of restrictions in causal attribution are discussed, since its logical requirements are covered by well known methodological principles. On the linguistic and communicative side, limitations and biases in causal ascription are introduced by (1) the three central concepts ('alcohol', 'cause', 'accidents') due to properties inherent in language; (2) the (often implicit) selection of boundary conditions; (3) heuristic inference rules; and (4) the tendency towards thematic closure in describing and explaining phenomena. It is suggested that social, psychological and interactional causal processes have been overlooked in attributing causal links between alcohol use, hazardous behaviour and accidents.

Accidents↗

PubMed2000

[Achievement of causal inference in the social medicine in Japan].

As a condition to achieving an agreement of recognition on the causal relationship in medicine, we firstly explained Hume's problem and counterfactual model. We, however, emphasized that we believe in the existence of causality on medical issues in our daily lives. Therefore, we illustrated conditions when we usually believe in causality. On the other hand, we criticized two well-known key phrases, "lack of mechanism in epidemiology" and "black box in epidemiology", which have often been used in Japan for skeptic viewpoints against epidemiologic methods even if epidemiology is often used to elucidate a causal effect in medicine in the world. We emphasized that a priori determinations of levels for inference of mechanism is necessary. And, the level and feature of mechanism should be defined in concrete expressions. After explanation of these basic concepts, we mentioned a classic view on specific diseases and non-specific diseases which have not been sufficiently discussed enough yet in Japan. As an example, we used the statements in the Japanese Compensation Law for the Health Effect by Environmental Pollution. In Japan, the classification of these diseases has been confused with that between manifestational criteria of diseases and causal criteria of them. We described the basic concepts to illustrate the causal relationship between non-specific disease and its exposure by using attached figures. Actually, we cannot recognize disease occurrence as a specific disease for several reasons. We indicated that we can recognize the magnitude of effect by causal relationships in medicine as a quantitative continuous variable.

Causality↗

PubMed2000

Interpreting epidemiological evidence: how meta-analysis and causal inference methods are related.

Interpreting observational epidemiological evidence can involve both the quantitative method of meta-analysis and the qualitative criteria-based method of causal inference. The relationships between these two methods are examined in terms of the capacity of meta-analysis to contribute to causal claims, with special emphasis on the most commonly used causal criteria: consistency, strength of association, dose-response, and plausibility. Although meta-analysis alone is not sufficient for making causal claims, it can provide a reproducible weighted average of the estimate of effect that seems better than the rules-of-thumb (e.g. majority rules and all-or-none) often used to assess consistency. A finding of statistical heterogeneity, however, need not preclude a conclusion of consistency (e.g. consistently greater than 1.0). For the criteria of strength of association and dose-response, meta-analysis provides more precise estimates, but the causal relevance of these estimates remains a matter of judgement. Finally, meta-analysis may be used to summarize evidence from biological, clinical, and social levels of knowledge, but combining evidence across levels is beyond its current capacity. Meta-analysis has a real but limited role in causal inference, adding to an understanding of some causal criteria. Meta-analysis may also point to sources of confounding or bias in its assessment of heterogeneity.

Causality↗

PubMedDate not supplied

[Causality in urologic research].

Clinical-epidemiological research may orient us about the causes of disease, the relationships among them, and the relative magnitudes of their effects. The objective of this article is to link the notion of cause with the basic clinical-epidemiological parameters. There are different models explaining causality. All of them present the possible etiologic explanations for the diseases, taking into consideration the current knowledge at the time they have been posed. We start from a purely determinist conception, understanding causality as a constant connection between two factors x and y, unique, and perfectly predictable. Currently, this model is inadequate to be applied to many diseases. Many researchers have modified the determinist model to explain the multiple causality of disease, posing the existence of associations of causal factors, more than single factors, being these associations treated as sufficient cause (i.e. as a group of minimal conditions and events that inevitably produce the disease). That determinist concept of causality is supplemented with the probabilistic concept. The theory of probability is used in it, as well as the related statistical, methods, to empirically evaluate a possible association that is believed causal. As a consequence of the lack of certainty of the prediction at the individual level, the theoretical notion of cause is replaced by the empirical concept of risk factor, referring to a variable which is considered to be related to the probability that one individual develops the disease. Causal inference in epidemiology is the logic development of a theory, based on observations and arguments that attribute the presence (association) of a disease to one or more risk factors. We will follow the principles posed by B. Hill for the complex process called scientific generalization. To correctly perform this relationship between our ideas and are observations it is absolutely important to start from a correct election of the study design with which the research is undertaken.

Biomedical Research↗

PubMedDate not supplied

[Causality link in criminal law: role of epidemiology].

This paper focusses on the role of epidemiology in demonstrating causality in criminal trials of toxic tort litigation. First of all, consideration is given of the specificity of the criminal trial and of the role of the epidemiologist as expert witness. As a second step the concept of causality is examined separating general from specific (individual level) causality. As regards general causality, strategies based on some criteria (example: Bradford-Hill criteria) are contrasted with approaches that do not consider causality a matter of science but one of health policy; and specific methods frequently used (meta-analysis, risk assessment, International Boards evaluation,....) are discussed, with special reference to the adoption of high-level standards and to the context of cross-examination. As regards individual level causality the difficulties of the epidemiologic approach to such evaluation are stressed, with special reference to topics like expected value, attributable risk, and probability of causation. All examples are taken from Italian court trials. A general comment on the difficulties of using the criminal trial (dominated by the "but for" rule) for toxic tort litigation and on the opportunity to switch to trials (civil, administrative) with less stringent causal rules ("more probable than not") is offered, with a consideration also of what are called "class actions".

Causality↗

PubMed1996

The practice of causal inference in cancer epidemiology.

Causal inference is an important link between the practice of cancer epidemiology and effective cancer prevention. Although many papers and epidemiology textbooks have vigorously debated theoretical issues in causal inference, almost no attention has been paid to the issue of how causal inference is practiced. In this paper, we review two series of review papers published between 1985 and 1994 to find answers to the following questions: which studies and prior review papers were cited, which causal criteria were used, and what causal conclusions and public health recommendations ensued. Fourteen published reviews on alcohol and breast cancer and 6 published reviews on vasectomy and prostate cancer were examined. For both series of reviews, nearly all available published studies were cited except for ecological studies and prior reviews. Sources of causal criteria were often not provided. When they appeared, all citations were either the 1964 Surgeon General's report or works of Austin Bradford Hill. Reviews often excluded and sometimes altered criteria without giving reasons for these changes. The criteria of consistency and strength of association were almost always used accompanied by dose-response and biological plausibility in a majority of reviews. The criterion of temporality, considered by many methodologists to be a necessary causal condition, was infrequently used. Confounding and bias were often added considerations. Public health recommendations were not discussed in nearly one-half of the reviews.

Breast Neoplasms↗