Controlling for causally relevant third variables

In 3 experiments, the authors tested the conditions under which 3rd variables are controlled for in making causal judgments. The authors hypothesized that 3rd variables are controlled for when the 3rd variables are themselves perceived as causal. In Experiment 1, the participants predicted test performance after seeing information about wearing a lucky garment, taking a test-preparation course, and staying up late. The course (perceived as more causally relevant) was controlled for more than was the garment (perceived as less causally relevant) in assessing the effectiveness of staying up late. In Experiments 2 and 3, to obviate the many alternative accounts that arise from the realistic cover story of Experiment 1, participants predicted flowers' blooming after the presentation or nonpresentation of liquids. When one liquid was trained as causal, it was controlled for more in judging another liquid than when it was trained as neutral. Overall, stimuli perceived as causal were controlled for more when judging other stimuli. The authors concluded that the effect of perceived causal relevance on causal conditionalizing is real and normatively reasonable.     

Causal structure learning over time: observations and interventions

Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent - the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal structures quickly and reliably when variables are temporally stable (Experiments 1 and 2). People use this strategy even when the cover story suggests that the trials are independent (Experiment 3). When observing variables over time, people believe that when a cause changes state, its effects likely change state, but an effect may change state due to an exogenous influence in which case its observed cause may not change state at the same time. People used this strategy to learn the direction of causal relations and a wide variety of causal structures (Experiments 4-6). Finally, considering exogenous influences responsible for the observed changes facilitates learning causal directionality (Experiment 7). Temporal reasoning may be the norm rather than the exception for causal learning and may reflect the way most events are experienced naturalistically.     

Non-bayesian inference: causal structure trumps correlation

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.     

Causal Relata: Tokens, Types, or Variables?

The literature on causation distinguishes between causal claims relating properties or types and causal claims relating individuals or tokens. Many authors maintain that corresponding to these two kinds of causal claims are two different kinds of causal relations. Whether to regard causal relations among variables as yet another variety of causation is also controversial. This essay maintains that causal relations obtain among tokens and that type causal claims are generalizations concerning causal relations among these tokens.     

Controlling for Causally Relevant Third Variables

In 3 experiments, the authors tested the conditions under which 3rd variables are controlled for in making causal judgments. The authors hypothesized that 3rd variables are controlled for when the 3rd variables are themselves perceived as causal. In Experiment 1, the participants predicted test performance after seeing information about wearing a lucky garment, taking a test-preparation course, and staying up late. The course (perceived as more causally relevant) was controlled for more than was the garment (perceived as less causally relevant) in assessing the effectiveness of staying up late. In Experiments 2 and 3, to obviate the many alternative accounts that arise from the realistic cover story of Experiment 1, participants predicted flowers' blooming after the presentation or non-presentation of liquids. When one liquid was trained as causal, it was controlled for more in judging another liquid than when it was trained as neutral. Overall, stimuli perceived as causal were controlled for more when judging other stimuli. The authors concluded that the effect of perceived causal relevance on causal conditionalizing is real and normatively reasonable.     

Causal models frame interpretation of mathematical equations

We offer evidence that people can construe mathematical relations as causal. The studies show that people can select the causal versions of equations and that their selections predict both what they consider most understandable and how they expect variables to influence one another. When asked to write down equations, people have a strong preference for the version that matches their causal model. Causal models serve to structure equations by determining the preferred order of variables: Causes should be on one side of an equality, and a single effect should appear on the other.     

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.     

Inferring action structure and causal relationships in continuous sequences of human action

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.     

Rethinking the Control of Nuisance Variables in Theory Testing

In testing hypotheses, researchers frequently have used multiple regression analysis to control for nuisance variables (i.e., potential confounding variables that are correlated with hypothesized causal variables). In this paper, we highlight limitations of this control strategy, and we discuss fundamental issues that should be considered in deciding whether to use it. Ultimately, we suggest the use of multiple regression analysis sometimes may not improve causal understanding and may actually limit the generalizability of results. Instead of the common practice of controlling for nuisance variables, we suggest that looking at shared variance frequently is a more appropriate test of a theoretical hypothesis.     

On the origin of personal causal theories

Detecting the causal relations among environmental events is an important facet of learning. Certain variables have been identified which influence both human causal attribution and animal learning: temporal priority, temporal and spatial contiguity, covariation and contingency, and prior experience. Recent research has continued to find distinct commonalities between the influence these variables have in the two domains, supporting a neo-Humean analysis of the origins of personal causal theories. The cues to causality determine which event relationships will be judged as causal; personal causal theories emerge as a result of these judgments and in turn affect future attributions. An examination of animal learning research motivates further extensions of the analogy. Researchers are encouraged to study real-time causal attributions, to study additional methodological analogies to conditioning paradigms, and to develop rich learning accounts of the acquisition of causal theories.     

Causal judgments about relations between multilevel variables

Three experiments presented stimulus information about cause and effect variables taking 3 quantitative values. Judgments tended to vary in accordance with considerations of conditions affecting the validity of causal inference from correlational data: whether causal candidates were presented simultaneously or in a temporal order such that one could affect the other and whether candidates were confounded with each other. The results supported a general hypothesis that causal judgments are moderated in accordance with acquired methodological intuitions. The fourth experiment showed that tendencies in correlation judgment were different from those in causal judgment, further supporting the hypothesis that causal judgment from multilevel variable information is, to some extent, determined by processes or conceptual frameworks specific to the domain of causal cognition.     

Sufficiency and Necessity Assumptions in Causal Structure Induction

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism (sufficiency or necessity; Experiment 1). These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally (Experiment 2) and appear to be domain‐general (Experiment 3). Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors.     

Correlation,partial correlation,and causation

Philosophers and scientists have maintained that causation, correlation, and “partial correlation” are essentially related. These views give rise to various rules of causal inference. This essay considers the claims of several philosophers and social scientists for causal systems with dichotomous variables. In section 2 important commonalities and differences are explicated among four major conceptions of correlation. In section 3 it is argued that whether correlation can serve as a measure of A's causal influence on B depends upon the conception of causation being used and upon certain background assumptions. In section 4 five major kinds of “partial correlation” are explicated, and some of the important relations are established among two conceptions of “partial correlation”, the conception of “screening off”, the conception of “partitioning”, and the measures of causal influence which have been suggested by advocates of path analysis or structural equation methods. In section 5 it is argued that whether any of these five conceptions of “partial correlation” can serve as a measure of causal influence depends upon the conception of causation being used and upon certain background assumptions. The important conclusion is that each of the approaches (considered here) to causal inference for causal systems with dichotomous variables stands in need of important qualifications and revisions if they are to be justified.     

The effect of representations of binary variables on judgment of influence

Studies concerned with judgments of contingency between binary variables have often ignored what the variables stand for. The two values of a binary variable can be represented as a prevailing state (nonevent) or as an active state (event). Judgments under the four conditions resulting from the combination of a binary input variable that can be represented as event-nonevent or event-event with an outcome variable that can be represented in the same way were obtained. It is shown in Experiment 1, that judgments of data sets which exhibit the same degree of covariation depend upon how the input and output variables are represented. In Experiment 2 the case where both the input and output variables are represented as event-nonevent is examined. Judgments were higher when the pairing of the input event was with the output event and the input nonevent with the output nonevent that when the pairing was of event with nonevent, suggesting a causal compatibility of event-event pairings and a causal incompatibility of event-nonevent pairings. Experiment 3 demonstrates that judgments of the strength of the relation between binary input and output variables is not based on the appropriate statistical measure, the difference between two conditional probabilities. The overall pattern of judgments in the three experiments is mainly explicable on the basis of two principles: (1) judgments tend to be based on the difference between confirming and disconfirming cases and (2) causal compatibility in the representation of the input and output variables plays a critical role.     

INFLUENCE OF SPOKEN LANGUAGE ON THE INITIAL ACQUISITION OF READING/WRITING: CRITICAL ANALYSIS OF VERBAL DEFICIT THEORY

This article presents the results of a quasi-experimental study of whether there exists a causal relationship between spoken language and the initial learning of reading/writing. The subjects were two matched samples each of 24 preschool pupils (boys and girls), controlling for certain relevant external variables. It was found that there was no causal relationship between the initial reading/writing performance and the language variables that have traditionally been regarded as relevant in facilitating the learning of reading/writing (vocabulary, articulation, and auditory memory). Phonemic awareness, however, was strongly causally related with the writing of simple words.     

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.     

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.     

Finding the Cause: Verbal Framing Helps Children Extract Causal Evidence Embedded in a Complex Scene

In making causal inferences, children must both identify a causal problem and selectively attend to meaningful evidence. Four experiments demonstrate that verbally framing an event (“Which animals make Lion laugh?”) helps 4-year-olds extract evidence from a complex scene to make accurate causal inferences. Whereas framing was unnecessary when evidence was isolated, children required it to extract and reason about evidence embedded in a more complex scene. Subtler framing stating the causal problem, but not highlighting the relevant variables, was equally effective. Simply making the causal relationship more perceptually obvious did facilitate children's inferences, but not to the level of verbal framing. These results illustrate how children's causal reasoning relies on scaffolding from adults.     

The theoretical status of latent variables

This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized.     

Further extensions of precision medicine to behavior analysis: A demonstration using functional communication training

The potential applicability of concepts and methods of the paradigm of precision medicine to the field of applied behavior analysis is only beginning to be explored. Both precision medicine and applied behavior analysis seek to understand and classify clinical problems through identification of their causal pathways. Both aim to develop treatments directly targeting those causal pathways, which also requires an understanding of the mechanisms by which treatments produce change (treatment-action pathways). In the current study, we extend the data-analytic methods and concepts described by Hagopian et al. (2018) toward the identification of variables that predict response to functional communication training (FCT). We discuss emerging conceptual issues, including the importance of distinguishing predictive behavioral markers from predictor variables based on their purported involvement in the causal or treatment-action pathways. Making these discriminations is a complex undertaking that requires knowledge of these mechanisms and how they interact.