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At least 19 recordsLinked to original sources
PubMed1999

Toward a causal realist account of causal understanding.

Within a limited domain, humans can perceive causal relations directly. The term causal realism is used to denote this psychological hypothesis. The domain of causal realism is in actions upon objects and haptic perception of the effects of those actions: When we act upon an object we cannot be mistaken about the fact that we are acting upon it and perceive the causal relation directly through mechanoreceptors. Experiences of actions upon objects give rise to causal knowledge that can be used in the interpretation of perceptual input. Phenomenal causality, the occurrence of causal impressions in visual perception, is a product of the application of acquired causal knowledge in the automatic perceptual interpretation of appropriate stimuli. Causal realism could constitute the foundation on which all causal perception, judgment, inference, attribution, and knowledge develop.

Adult↗
PubMed2004

Causal discovery using a Bayesian local causal discovery algorithm.

This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the "locality" for the identification of pair-wise causal relationships. BLCD takes as input a dataset and outputs potential causes of the form variable X causally influences variable Y. Identification of the causal factors of diseases and outcomes, can help formulate better management, prevention and control strategies for the improvement of health care. In this study we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 re-cords randomly selected from the whole dataset. Each record had maternal, paternal and child factors and the outcome at the end of the first year--whether the infant survived or not. Using the infant birth and death dataset as input, BLCD out-put six purported causal relationships. Three out of the six relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample.

Algorithms↗
PubMed2004

Cue interaction and judgments of causality: contributions of causal and associative processes.

In four experiments, the predictions made by causal model theory and the Rescorla-Wagner model were tested by using a cue interaction paradigm that measures the relative response to a given event based on the influence or salience of an alternative event. Experiments 1 and 2 uncorrelated two variables that have typically been confounded in the literature (causal order and the number of cues and outcomes) and demonstrated that overall contingency judgments are influenced by the causal structure of the events. Experiment 3 showed that trial-by-trial prediction responses, a second measure of causal assessment, were not influenced by the causal structure of the described events. Experiment 4 revealed that participants became less sensitive to the influence of the causal structure in both their ratings and their predictions as trials progressed. Thus, two experiments provided evidence for high-level (causal reasoning) processes, and two experiments provided evidence for low-level (associative) processes. We argue that both factors influence causal assessment, depending on what is being asked about the events and participants' experience with those events.

Association Learning↗
PubMed2005

Dissociating processes supporting causal perception and causal inference in the brain.

An understanding of relations between causes and effects is essential for making sense of the dynamic physical world. It has been argued that this understanding of causality depends on both perceptual and inferential components. To investigate whether causal perception and causal inference rely on common or on distinct processes, the authors tested 2 callosotomy (split-brain) patients and a group of neurologically intact participants. The authors show that the direct perception of causality and the ability to infer causality depend on different hemispheres of the divided brain. This finding implies that understanding causality is not a unitary process and that causal perception and causal inference can proceed independently.

Chi-Square Distribution↗
PubMed1997

Causal or casual? The role of causality assessment in pharmacovigilance.

As with any other study method, 'spontaneous reporting' in pharmacovigilance is a process of data acquisition, assessment, presentation and interpretation. The provision of information (i.e. of interpreted data) concerning previously unknown, or otherwise important adverse drug reactions is a major goal. The assessment of case reports in spontaneous reporting takes place in 2 steps: first the assessment of each case individually, and secondly the interpretation of the aggregated data. The latter step is only completed for a minority of case reports, such as when actions or measures are deemed necessary. Uncertainty in case reports regarding the involvement of the suspected drugs is an inherent drawback of spontaneous reporting. Standardised case-causality assessment has become a routine at pharmacovigilance centres around the world. It aims at a decrease in ambiguity of the data and plays a role in data exchange and the prevention of erroneous conclusions. A variety of systems for standardised causality assessment have been developed, ranging from short questionnaires to comprehensive algorithms. Since none of the available assessment systems has been validated (i.e. shown to consistently and reproducibly produce a fair approximation of the truth), causality assessment has only limited scientific value. Causality assessment neither eliminates nor quantifies uncertainty but, at best, categorises it in a semiquantitative way. Routine causality assessment is usually part of the first step in case assessment, and is based on a general system that is intended for all reactions and all drugs. During the subsequent phase of aggregated assessment, causality assessment is likely to be repeated and the use of a specific aetiological-diagnostic system may be more appropriate. It may be recommended to restrict case-causality assessment to selected case reports that are likely to play an active role in pharmacovigilance and to use specific systems, adapted to the reaction or problem involved. It is an inherent limitation of spontaneous reporting that, with the exception of rare proof-positive case reports, conclusive evidence cannot usually be produced. Standardised causality assessment has not really changed this situation. As a rule, confirmation of the connection between a drug and an adverse reaction requires further analytical or experimental study.

Adverse Drug Reaction Reporting Systems↗
PubMed2005

Associative and causal reasoning accounts of causal induction: symmetries and asymmetries in predictive and diagnostic inferences.

Associative and causal reasoning accounts are probably the two most influential types of accounts of causal reasoning processes. Only causal reasoning accounts predict certain asymmetries between predictive (i.e., reasoning from causes to effects) and diagnostic (i.e., reasoning from effects to causes) inferences regarding cue-interaction phenomena (e.g., the overshadowing effect). In the experiments reported here, we attempted to delimit the conditions under which these asymmetries occur. The results show that unless participants perceived the relevance of causal information to solving the task, predictive and diagnostic inferences were symmetrical. Specifically, Experiments 1A and 1B showed that implicitly stressing the relevance of causal information by having participants review the instructions favored the presence of asymmetries between predictive and diagnostic situations. In addition, Experiment 2 showed that explicitly stressing the relevance of causal information by stating the importance of the causal role of events after the instructions were given also favored the asymmetry.

Adult↗
PubMed2004

A theory of causal learning in children: causal maps and Bayes nets.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Adult↗
PubMed2002

[Brief causal relations in EEG based on adaptive Granger causality].

Commonly, coherence and correlation are used to describe interrelations between EEG signals. But, on this basis, the investigation of causality or direction of interrelations is not possible. The general idea of causality between two signals may be expressed in terms of upgrading the predictability of one signal bye the knowledge of the past of the other signal. The best established approach in this context is the so-called Granger causality. The study present an adaptive estimation of Granger causality, which allows to detect dynamic causal relations within time intervals of less 100 ms. The time-variant Granger causality is applied to EEG data of the Stroop task. It could be shown, that conflict situation generates a dense web of directed interactions from posterior to anterior cortical areas.

Adult↗
PubMed2001

Causal knowledge and categories: the effects of causal beliefs on categorization, induction, and similarity.

Despite the recent interest in the theoretical knowledge embedded in human representations of categories, little research has systematically manipulated the structure of such knowledge. Across four experiments this study assessed the effects of interattribute causal laws on a number of category-based judgments. The authors found that (a) any attribute occupying a central position in a network of causal relationships comes to dominate category membership, (b) combinations of attribute values are important to category membership to the extent they jointly confirm or violate the causal laws, and (c) the presence of causal knowledge affects the induction of new properties to the category. These effects were a result of the causal laws, rather than the empirical correlations produced by those laws. Implications for the doctrine of psychological essentialism, similarity-based models of categorization, and the representation of causal knowledge are discussed.

Adult↗
PubMed2004

Illusory causal crescents: misperceived spatial relations due to perceived causality.

When an object A moves toward an object B until they are adjacent, at which point A stops and B starts moving, we often see a collision--ie we see A as the cause of B's motion. The spatiotemporal parameters which mediate the perception of causality have been explored in many studies, but this work is seldom related to other aspects of perception. Here we report a novel illusion, wherein the perception of causality affects the perceived spatial relations among two objects involved in a collision event: observers systematically underestimate the amount of overlap between two items in an event which is seen as a causal collision. This occurs even when the causal nature of the event is induced by a surrounding context, such that estimates of the amount of overlap in the very same event are much improved when the event is displayed in isolation, without a 'causal' interpretation. This illusion implies that the perception of causality does not proceed completely independently of other visual processes, but can affect the perception of other spatial properties.

Humans↗
PubMed2002

Perceiving a strong causal relation in a weak contingency: further investigation of the evidential evaluation model of causal judgement.

Contingency information is information about the occurrence or nonoccurrence of a certain effect in the presence or absence of a candidate cause. An objective measure of contingency is the deltaP rule, which involves subtracting the probability of occurrence of an effect when a causal candidate is absent from the probability of occurrence of the effect when the candidate is present. Causal judgements conform closely to deltaP but deviate from it under certain circumstances. Three experiments show that such deviations can be predicted by a model of causal judgement that has two components: a rule of evidence, that causal judgement is a function of the proportion of relevant instances that are judged to be confirmatory for the causal candidate, and a tendency for information about instances in which the candidate is present to have greater effect on judgement than instances in which the candidate is absent. Two experiments demonstrate how this model accounts for some recently published findings. A third experiment shows that it is possible to use the model to predict the occurrence of high causal judgements when the objective contingency is close to zero.

Attitude to Health↗
PubMed2007

Knowing who dunnit: Infants identify the causal agent in an unseen causal interaction.

Preverbal infants can represent the causal structure of events, including distinguishing the agentive and receptive roles and categorizing entities according to stable causal dispositions. This study investigated how infants combine these 2 kinds of causal inference. In Experiments 1 and 2, 9.5-month-olds used the position of a human hand or a novel puppet (causal agents), but not a toy train (an inert object), to predict the subsequent motion of a beanbag. Conversely, in Experiment 3, 10- and 7-month-olds used the motion of the beanbag to infer the position of a hand but not of a toy block. These data suggest that preverbal infants expect a causal agent as the source of motion of an inert object.

Association Learning↗
PubMed2005

Causal thinking and causal language in epidemiology: it's in the details.

Although epidemiology is necessarily involved with elucidating causal processes, we argue that there is little practical need, having described an epidemiological result, to then explicitly label it as causal (or not). Doing so is a convention which obscures the valuable core work of epidemiology as an important constituent of public health practice. We discuss another approach which emphasizes the public health "use value" of research findings in regard to prediction and intervention independent from explicit metaphysical causal claims. Examples are drawn from smoking and lung cancer, with particular focus on the original 1964 Surgeon General's report on smoking and the new version released in 2004. The intent is to help the epidemiologist focus on the pertinent implications of research, which, from a public health point of view, in large part entails the ability to predict and to intervene. Further discussion will center on the importance of differentiating between technical/practical uses of causal language, as might be used in structural equations or marginal structural modeling, and more foundational notions of cause. We show that statistical/epidemiological results, such as "smoking two packs a day increases risk of lung cancer by 10 times" are in themselves a kind of causal argument that are not in need of additional support from relatively ambiguous language such as "smoking causes lung cancer." We will show that the confusion stemming from the use of this latter statement is more than mere semantics. Our goal is to allow researchers to feel more confident in the power of their research to tell a convincing story without resorting to metaphysical/unsupportable notions of cause.

Journal Article↗
PubMed1995

Use of prior beliefs in the assignment of causal roles: causal powers versus regularity-based accounts.

There is a tradition of models of causal judgment in which causes and other causal roles are defined and identified in terms of empirical patterns of association with effects. In the present experiments, results conflicting with the predictions of such models were obtained. In one experiment, subjects judged that an interpretation in which a factor constantly present was identified as the cause was more likely than was an interpretation in which a perfect positive covariate was identified as the cause. In a second experiment, possible effects of prior beliefs about covariation were controlled and similar findings were obtained in two out of three scenarios. These results favor the idea that people make causal judgments by applying preexisting beliefs framed in terms of causal concepts, such as causal powers, and in ways that cannot be accounted for by models in the empiricist tradition.

Adolescent↗
PubMed2001

Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation.

Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3- and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning.

Adult↗
PubMed2006

Perceiving causality after the fact: postdiction in the temporal dynamics of causal perception.

In simple dynamic events we can easily perceive not only motion, but also higher-level properties such as causality, as when we see one object collide with another. Several researchers have suggested that such causal perception is an automatic and stimulus-driven process, sensitive only to particular sorts of visual information, and a major research project has been to uncover the nature of these visual cues. Here, rather than investigating what information affects causal perception, we instead explore the temporal dynamics of when certain types of information are used. Surprisingly, we find that certain visual events can determine whether we perceive a collision in an ambiguous situation even when those events occur after the moment of potential 'impact' in the putative collision has already passed. This illustrates a type of postdictive perception: our conscious perception of the world is not an instantaneous moment-by-moment construction, but rather is formed by integrating information presented within short temporal windows, so that new information which is obtained can influence the immediate past in our conscious awareness. Such effects have been previously demonstrated for low-level motion phenomena, but the present results demonstrate that postdictive processes can influence higher-level event perception. These findings help to characterize not only the 'rules' of causal perception, but also the temporal dynamics of how and when those rules operate.

Causality↗
PubMed2001

Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance.

We consider the question of evaluating causal relations among neurobiological signals. In particular, we study the relation between the directed transfer function (DTF) and the well-accepted Granger causality, and show that DTF can be interpreted within the framework of Granger causality. In addition, we propose a method to assess the significance of causality measures. Finally, we demonstrate the applications of these measures to simulated data and actual neurobiological recordings.

Action Potentials↗