Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).     

Illness perception in people with chronic obstructive pulmonary disease

Illness perception (IP) concerns how patients evaluate living with a disease. To get a broader understanding of IP in patients with chronic obstructive pulmonary disease (COPD), we investigated whether breathlessness is an important precursor of IP and whether IP in its turn is related to mental health, physical health and global quality of life (QOL). One hundred and fifty‐four patients with COPD participated in a cross‐sectional survey. Participants underwent pulmonary function testing, provided socio‐demographic and clinical information, and completed the following standardized instruments: Brief Illness Perception Questionnaire, Respiratory Quality of Life Questionnaire, Short‐Form 12 Health Survey and the Quality of Life Scale. Multiple regression analyses were performed. A high IP score indicates that a patient believes that his/her illness represents a threat. Participants with a high score on the IP dimensions consequences, identity, concern and emotional representation, experienced more breathlessness. High scores on the IP dimensions consequences, identity and concern were associated with impaired physical health and high scores on the IP dimensions consequences, identity and emotional representation were associated with impaired mental health. Impaired global QOL was associated with high scores on the IP dimensions consequences, identity, concern, coherence and emotional representation. The strength of the associations between breathlessness and physical/mental health and global QOL decreased when certain dimensions of IP were included as predictors, indicating that IP to some extent acts as a mediating factor. These findings may have practical implications of patient counselling by helping COPD patients to cope with their disease by restructuring their personal models of illness.     

A Process Model of Causal Reasoning

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process-level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it.     

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.     

When is a cause the “same”? Incoherent generalization across contexts

A theory or model of cause such as Cheng's power (p) allows people to predict the effectiveness of a cause in a different causal context from the one in which they observed its actions. Liljeholm and Cheng demonstrated that people could detect differences in the effectiveness of the cause when causal power varied across contexts of different outcome base rates, but that they did not detect similar changes when only the cause–outcome contingency, ?p, but not power, varied. However, their procedure allowed participants to simplify the causal scenarios and consider only a subsample of observations with a base rate of zero. This confounds p, ?p, and the probability of an outcome (O) given a cause (C), P(O|C). Furthermore, the contingencies that they used confounded p and P(O|C) in the overall sample. Following the work of Liljeholm and Cheng, we examined whether causal induction in a wider range of situations follows the principles suggested by Cheng. Experiments 1a and 1b compared the procedure used by Liljeholm and Cheng with one that did not allow the sample of observations to be simplified. Experiments 2a and 2b compared the same two procedures using contingencies that controlled for P(O|C). The results indicated that, if the possibility of converting all contexts to a zero base rate situation was avoided, people were sensitive to changes in P(O|C), p, and ?p when each of these was varied. This is inconsistent with Liljeholm and Cheng's conclusion that people detect only changes in p. These results question the idea that people naturally extract the metric or model of cause from their observation of stochastic events and then, reasonably exclusively, use this theory of a causal mechanism, or for that matter any simple normative theory, to generalize their experience to alternative contexts.     

Categorization as causal reasoning⋆

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory.     

The mental representation of causal conditional reasoning: mental models or causal models

In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving pairs of conditionals—such as if P₁then Q and if P₂then Q—are considered. From a causal perspective, the causal direction of these conditionals is critical: are the P_icauses of Q; or symptoms caused byQ. The rich variety of inference patterns can naturally be modelled by Bayesian networks. A pair of causal conditionals where Q is an effect corresponds to a “collider” structure where the two causes (P_i) converge on a common effect. In contrast, a pair of causal conditionals where Q is a cause corresponds to a network where two effects (P_i) diverge from a common cause. Very different predictions are made by fully explicit or initial mental models interpretations. These predictions were tested in two experiments, each of which yielded data most consistent with causal model theory, rather than with mental models.     

There aren't plenty more fish in the sea: A causal network approach

The current research investigated how lay representations of the causes of an environmental problem may underlie individuals' reasoning about the issue. Naïve participants completed an experiment that involved two main tasks. The causal diagram task required participants to depict the causal relations between a set of factors related to overfishing and to estimate the strength of these relations. The counterfactual task required participants to judge the effect of counterfactual suppositions based on the diagrammed factors. We explored two major questions: (1) what is the relation between individual causal models and counterfactual judgments? Consistent with previous findings (e.g., Green et al., 1998, Br. J. Soc. Psychology, 37, 415), these judgments were best explained by a combination of the strength of both direct and indirect causal paths. (2) To what extent do people use two‐way causal thinking when reasoning about an environmental problem? In contrast to previous research (e.g., White, 2008, Appl. Cogn. Psychology, 22, 559), analyses based on individual causal networks revealed the presence of numerous feedback loops. The studies support the value of analysing individual causal models in contrast to consensual representations. Theoretical and practical implications are discussed in relation to causal reasoning as well as environmental psychology.     

Agential obligation as non-agential personal obligation plus agency

I explore various ways of integrating the framework for predeterminism, agency, and ability in [P. McNamara, Nordic J. Philos. Logic 5 (2) (2000) 135] with a framework for obligations. However, the agential obligation operator explored here is defined in terms of a non-agential yet personal obligation operator and a non-deontic (and non-normal) agency operator. This is contrary to the main current trend, which assumes statements of personal obligation always take agential complements. Instead, I take the basic form to be an agent's being obligated to be such that p. I sketch some logics for agential obligation based on personal obligation and agency, first in a fairly familiar context that rules out conflicting personal obligations (and derivatively, conflicting agential obligations), and then in contexts that do allow for conflicts (of both sorts). Finally, a solution to van Fraassen's puzzle is sketched, and an important theorem is proved.     

The Causal Chain Problem

This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between the two models with respect to model expansions. This asymmetry might constitute a basis on which to disambiguate corresponding causal inferences on non-empirical grounds.

Bridging the gap: Causality-at-a-distance in children's categorization and inferences about internal properties

Previous research has shown that preschoolers extend labels and internal properties of objects based on those objects’ causal properties, even when the causal properties conflict with the objects’ perceptual appearance [Nazzi, T., & Gopnik, A. (2000). A shift in children's use of perceptual and causal cues to categorization. Developmental Science, 3, 389–396; Sobel, D. M., Yoachim, C. M., Gopnik, A., Meltzoff, A. N., & Blumenthal, E. J. (2007). The blicket within: Preschoolers’ inferences about insides and causes. Journal of Cognition and Development, 8, 159–182]. These studies, however, only presented causal relations that acted on contact. In two studies, contact causality was replaced by distance causality. In contrast to the contact causality case, 4- and 5-year-olds extended labels to objects with similar perceptual properties over objects with similar causal properties when those properties acted at a distance. When children were asked to make inferences about object's internal properties, they were more likely to make causal responses, with 5-year-olds doing so to a greater extent than 4-year-olds. In a second study, 4-year-olds registered causal properties that acted at a distance and used them to make inferences when no perceptual conflict was present. These results support a hypothesis that young children develop an understanding of the specific mechanisms that link causal relations.     

What mediation analysis can (not) do

The present article is concerned with a common misunderstanding in the interpretation of statistical mediation analyses. These procedures can be sensibly used to examine the degree to which a third variable (Z) accounts for the influence of an independent (X) on a dependent variable (Y) conditional on the assumption that Z actually is a mediator. However, conversely, a significant mediation analysis result does not prove that Z is a mediator. This obvious but often neglected insight is substantiated in a simulation study. Using different causal models for generating Z (genuine mediator, spurious mediator, correlate of the dependent measure, manipulation check) it is shown that significant mediation tests do not allow researchers to identify unique mediators, or to distinguish between alternative causal models. This basic insight, although well understood by experts in statistics, is persistently ignored in the empirical literature and in the reviewing process of even the most selective journals.     

Contiguity and the causal theory of memory

In Memory: A Philosophical Study, Bernecker argues for an account of contiguity. This Contiguity View is meant to solve relearning and prompting, wayward causation problems plaguing the causal theory of memory. I argue that Bernecker’s Contiguity View fails in this task. Contiguity is too weak to prevent relearning and too strong to allow prompting. These failures illustrate a problem inherent in accounts of memory causation. Relearning and prompting are both causal relations, wayward only with respect to our interest in specifying remembering’s requirements. Solving them requires saying more about remembering, not causation. I conclude by sketching such an account.     

The Metaphysics of Causal Models

This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late pre?mption and other cases that have proved problematic for causal models.

The larger the cause,the larger the effect: evidence of speed judgment biases in causal scenarios

ABSTRACT

When two motions appear to be causally related, the spatiotemporal features of motions are sometimes distorted in order to increase the consistency with causal impressions. Here, in four experiments, we tested if varying the speed of an object A could affect the judged speed of an object B that appeared to be causally related to A. Participants were presented with classic launching stimuli (Experiment 1), a variant of launching stimuli in which A could move with uniformly accelerated or decelerated motion (Experiment 2), non-launching stimuli that elicited a causal impression (Experiment 3), and stimuli showing a three-object launching event (Experiment 4). Main results showed that the judged speed of B was systematically biased towards the speed of A, and moreover that the judged speed of B depended on the average speed of A, rather than on the speed of A at the moment of collision as it would be predicted by Newtonian mechanics. The results are consistent with the hypothesis that internal representations of causal events based on property transmission (for instance, impetus) can affect judgments of the low-level properties of causal scenarios.     

Time and Singular Causation—A Computational Model

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co-occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model.     

Error probabilities for inference of causal directions

A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness assumptions, the procedures can only be pointwise but not uniformly consistent without substantial background knowledge. This implies the impossibility of choosing a finite sample size to control the worst case error probabilities. In this paper, I consider the simpler task of inferring causal directions when the skeleton of the causal structure is known, and establish a similarly negative result concerning the possibility of controlling error probabilities. Although the result is negative in form, it has an interesting positive implication for causal discovery methods.     

When functions and causes compete

The discounting principle states that ‘the role of a given cause in producing a given effect is discounted if other plausible causes are present’ (Kelley, 1972 Kelley, H. H. 1972. “Attribution theory in social psychology”. In Attribution: Perceiving the causes of behaviour, Edited by: Jones, E., Kanouse, D., Kelley, H., Nisbett, R., Valins, S. and Weiner, B. 1–26. Morristown, NJ: General Learning Press.  [Google Scholar], p. 8). The principle has only been tested with cases where the two explanations are of the same kind (i.e., causal explanations). However, explanations of properties of objects, people, or events often involve functions. Zebras have stripes in order to be better camouflaged. Humans have eyebrows to keep sweat from running into their eyes. Adrenaline is secreted in order to modulate fight and flight responses. Thus, what happens when we are faced with two different kinds of explanation for the same property: one functional and one causal? People evaluated explanations of properties for natural kinds and artefacts. Functional explanations were discounted in favour of causal explanations, however this was only true for properties of artefacts. The presence of an alternative explanation for properties of natural kinds did not affect the plausibility of either kind of explanation.     

Independence and dependence in human causal reasoning

Causal graphical models (CGMs) are a popular formalism used to model human causal reasoning and learning. The key property of CGMs is the causal Markov condition, which stipulates patterns of independence and dependence among causally related variables. Five experiments found that while adult’s causal inferences exhibited aspects of veridical causal reasoning, they also exhibited a small but tenacious tendency to violate the Markov condition. They also failed to exhibit robust discounting in which the presence of one cause as an explanation of an effect makes the presence of another less likely. Instead, subjects often reasoned “associatively,” that is, assumed that the presence of one variable implied the presence of other, causally related variables, even those that were (according to the Markov condition) conditionally independent. This tendency was unaffected by manipulations (e.g., response deadlines) known to influence fast and intuitive reasoning processes, suggesting that an associative response to a causal reasoning question is sometimes the product of careful and deliberate thinking. That about 60% of the erroneous associative inferences were made by about a quarter of the subjects suggests the presence of substantial individual differences in this tendency. There was also evidence that inferences were influenced by subjects’ assumptions about factors that disable causal relations and their use of a conjunctive reasoning strategy. Theories that strive to provide high fidelity accounts of human causal reasoning will need to relax the independence constraints imposed by CGMs.