Choosing Optimal Causal Backgrounds for Causal Discovery

Cognitive Science, 2016

Although the causal graphical model framework has achieved considerable success accounting for causal learning data, application of that formalism to multi-cause situations assumes that people are insensitive to the statistical properties of the causes themselves. The present experiment tests this assumption by first instructing subjects on a causal model consisting of two independent and generative causes and then requesting them to make data likelihood judgments, that is, to estimate the probability of some data given the model. The correlation between the causes in the data was either positive, zero, or negative. The data was judged as most likely in the positive condition and least likely in the negative condition, a finding that obtained even though all other statistical properties of the data (e.g., causal strengths, outcome density) were controlled. These results pose a problem for current models of causal learning. Hypothesis testing occupies a central role in learning theor...

The causal impact of an observable cause can only be esti- mated if assumptions are made about the presence and impact of possible additional unobservable causes. Current theories of causal reasoning make different assumptions about hidden causes. Some views assume that hidden causes are always present, others that they are independent of the observed causes. In two experiments we assessed people's assumptions about the occurrence and statistical relations involving a hid- den cause. In the experiments, participants either only ob- served a cause or actively manipulated it. We assessed par- ticipants' assumption online after each learning trial and at the end of the learning phase. The results show an interesting dissociation. Whereas there was a tendency to assume nega- tive correlation in the online judgments, the final judgments tended more in the direction of an independence assumption. It could also be shown that the judgments were generally co- herent with the learni...

Journal of Experimental Social Psychology, 2002

Three studies examine the hypothesis that people spontaneously (i.e., unintentionally and without awareness of doing so) infer causes (the Spontaneous Causal Inference, or SCI, hypothesis). Using a cued-recall paradigm, Study 1 examines whether SCIs occur and Study 2 allows for a comparison between implicitly inferred and explicitly mentioned causes. Study 3 examines whether SCIs can be fully explained in terms of spreading activation to general, abstract schemes.

Learning and Motivation, 2004

This work aimed at demonstrating, first, that na€ ıve reasoners are able to infer the existence of a relationship between two events that have never been presented together and, second, the sensitivity of such inference to the causal structure of the task. In all experiments, naive participants judged the strength of the causal link between a cue A and an outcome O in a first phase and between a second cue B and the same outcome O in a second phase. In the final test, participants estimated the degree of correlation between the two cues, A and B. Participants perceived the two cues as significantly more highly correlated when they were effects of a common potential cause (Experiment 1a and 2) than when they were potential causes of a common effect (Experiment 1b and 2). This effect of causal directionality on inferred correlation points out the influence of mental models on human causal detection and learning, as proposed by recent theoretical models.

Electronic Workshops in Computing, 2009

Motivation-This paper describes the initial results of a naturalistic inquiry into the way people derive causal inferences. Research approach-We examined media accounts of economic, political, military, and sports incidents to determine the types of causal explanations that are commonly invoked. Findings-We found two interacting processes at work: the identification of potential causes and the framing of these causes into explanations. Explanations took several forms: abstractions, events, lists (undifferentiated collections of partial causes), conditions, and stories (complex mechanisms linking several causes). Originality-Causal reasoning in "the real world" is both different from and far richer than the formal causal accounts found in philosophy, and from the determinate search for causes during scientific problem solving. Takeaway message-By understanding the way causal reasoning is done in natural settings we should be better able to help decision makers diagnose problems and anticipate consequences.

Previous work showed that people"s causal judgments are modeled better as estimates of the probability that a causal relationship exists (a qualitative inference) than as estimates of the strength of that relationship (a quantitative inference). Here, using a novel task, we present experimental evidence in support of the importance of qualitative causal inference. Our findings cannot be explained through the use of parameter estimation and related quantitative inference. These findings suggest the role of qualitative inference in causal reasoning has been understudied despite its unique role in cognition. Further, we suggest these findings open interesting questions about the role of qualitative inference in many domains.

Cognitive science

It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional p...

Two competing psychological models of causal strength estimation make different predictions regarding when simple causal hypotheses will be rejected in favor of more complex ones. Two experiments tested these predictions, employing a novel method for indirectly assessing perceived causal strength. In both experiments, the task required a judgment regarding the existence of an interaction between a candidate cause and unobserved background causes. Results indicate that reasoners revise simple causal hypotheses based on the mental construct of causal power .

2019

People without any advanced training can make deductions about abstract causal relations. For instance, suppose you learn that habituation causes seriation, and that seriation prevents methylation. The vast majority of reasoners infer that habituation prevents methylation. Cognitive scientists disagree on the mechanisms that underlie causal reasoning, but many argue that people can mentally simulate causal interactions. We describe a novel algorithm that makes domain-general causal inferences. The algorithm constructs small-scale iconic simulations of causal relations, and so it implements the “model” theory of causal reasoning (Goldvarg & JohnsonLaird, 2001; Johnson-Laird & Khemlani, 2017). It distinguishes between three different causal relations: causes, enabling conditions, and preventions. And, it can draw inferences about both orthodox relations (habituation prevents methylation) and omissive causes (the failure to habituate prevents methylation). To test the algorithm, we sub...

Argument & Computation, 2013

The paper explores the idea that causality-based probability judgments are determined by two competing drives: one towards veridicality and one towards effort reduction. Participants were taught the causal structure of novel categories and asked to make predictive and diagnostic probability judgments about the features of category exemplars. We found that participants violated the predictions of a normative causal Bayesian network model because they ignored relevant variables (Experiments 1-3) and because they failed to integrate over hidden variables (Experiment 2). When the task was made easier by stating whether alternative causes were present or absent as opposed to uncertain, judgments approximated the normative predictions (Experiment 3). We conclude that augmenting the popular causal Bayes net computational framework with cognitive shortcuts that reduce processing demands can provide a more complete account of causal inference.