Causal Judgments Research Articles

Thinking Structurally: A Cognitive Framework for Understanding How People Attribute Inequality to Structural Causes

To make accurate causal inferences about social-group inequalities, people must consider structural causes. Structural causes are a distinct type of extrinsic cause—they are stable, interconnected societal forces that systematically advantage some social groups and disadvantage others. We propose a new cognitive framework to specify how people attribute inequality to structural causes. This framework is rooted in counterfactual theories of causal judgment and suggests that people will recognize structural factors as causal when they are perceived as “difference-making” for inequality above and beyond any intrinsic causes. Building on this foundation, our framework makes the following contributions. First, we propose specific types of evidence that support difference-making inferences about structural factors: within-group change (i.e., observing that disadvantaged groups’ outcomes improve under better societal conditions) and well-matched between-group comparisons (i.e., observing that advantaged group members, who have similar baseline traits to the disadvantaged group, experience more favorable societal conditions and life outcomes). Second, we consider contextual, cognitive, and motivational barriers that may complicate the availability and acceptance of this evidence. We conclude by exploring how the framework might be applied in future research examining people’s causal inferences about inequality.

What over When in causal agency: Causal experience prioritizes outcome prediction over temporal priority

Prior expectations strongly structure the way we perceive the world and ourselves. For instance, action-outcome prediction can modulate time perception and causal experience. We designed a study that allowed us to investigate whether action-outcome prediction has similar effects on time perception and intentional causality. Participants viewed a stimulus that was consistent or inconsistent with the action they, or another agent executed. The stimulus preceded or followed these actions and participants reported simultaneity or causal judgments. Observers were more likely to report the consistent outcomes as being generated by the action, even when the outcomes actually preceded the action. However, outcome consistency did not modulate simultaneity judgments. These results shed insight on the relationship between time and causal experience. It suggests that time perception and causal experience do not rely in the same way on temporal information, the latter being more permeable to contextual cues such as action-outcome consistency.

The Magnitude Heuristic: Larger Differences Increase Perceived Causality

AbstractWith the rise of machine learning and “big data,” many large yet spurious relationships between variables are discovered, leveraged by marketing communications, and publicized in the media. Thus, consumers are increasingly exposed to many large-magnitude relationships between variables that do not signal causal effects. This exposure may carry a substantial cost. Seven studies demonstrate that the magnitudes of relationships between variables can distort consumers’ judgments about whether those relationships reflect causal effects. Specifically, consumers often use a magnitude heuristic: consumers infer that relationships with larger perceived magnitudes are more likely to reflect causal effects, even when this is not true (and even when relationships’ correlations are held constant). In many situations, relying on the magnitude heuristic will distort causality judgments, such as when large-magnitude relationships between variables are spurious, or when normatively extraneous factors (e.g., reference points) distort perceptions of magnitudes. Moreover, magnitude-distorted (mis)perceptions of causality, in turn, distort consumers’ purchase and consumption decisions. Since consumers often encounter spurious relationships with large magnitudes in the health domain and in other consequential domains, the magnitude heuristic is likely to lead to biases in some of consumers’ most important decisions.

Explanations and Causal Judgments Are Differentially Sensitive to Covariation and Mechanism Information.

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to be more sensitive to mechanism and less sensitive to covariation – even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition.

The 1964 US Surgeon General's report on smoking and health.

The 1964 report of the US Surgeon General on smoking and health made an authoritative case that tobacco smoking was a cause of premature deaths from lung cancer and chronic bronchitis. It justified the practice of drawing causal inferences from epidemiological studies, specified influential criteria for doing so, and made use of an early form of meta-analysis.

An interaction effect of norm violations on causal judgment

Existing research has shown that norm violations influence causal judg- ments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interac- tion pattern in people's judgments. Specifically, it predicts that when people are judging the degree to which a particular factor is a cause, there should be an interaction between (a) the degree to which that factor violates a norm and (b) the degree to which another factor in the situation violates norms. A study of moral norms (N=1000) and norms of proper functioning (N=3000) revealed robust evidence for the predicted interaction effect. The implications of these patterns for existing theories of causal judgments is discussed.

Inference from explanation.

What do we communicate with causal explanations? Upon being told, "E because C", a person might learn that C and E both occurred, and perhaps that there is a causal relationship between C and E. In fact, causal explanations systematically disclose much more than this basic information. Here, we offer a communication-theoretic account of explanation that makes specific predictions about the kinds of inferences people draw from others' explanations. We test these predictions in a case study involving the role of norms and causal structure. In Experiment 1, we demonstrate that people infer the normality of a cause from an explanation when they know the underlying causal structure. In Experiment 2, we show that people infer the causal structure from an explanation if they know the normality of the cited cause. We find these patterns both for scenarios that manipulate the statistical and prescriptive normality of events. Finally, we consider how the communicative function of explanations, as highlighted in this series of experiments, may help to elucidate the distinctive roles that normality and causal structure play in causal judgment, paving the way toward a more comprehensive account of causal explanation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal reasoning without mechanism.

Unobservable mechanisms that tie causes to their effects generate observable events. How can one make inferences about hidden causal structures? This paper introduces the domain-matching heuristic to explain how humans perform causal reasoning when lacking mechanistic knowledge. We posit that people reduce the otherwise vast space of possible causal relations by focusing only on the likeliest ones. When thinking about a cause, people tend to think about possible effects that participate in the same domain, and vice versa. To explore the specific domains that people use, we asked people to cluster artifacts. The analyses revealed three commonly employed mechanism domains: the mechanical, chemical, and electromagnetic. Using these domains, we tested the domain-matching heuristic by testing adults' and children's causal attribution, prediction, judgment, and subjective understanding. We found that people's responses conform with domain-matching. These results provide evidence for a heuristic that explains how people engage in causal reasoning without directly appealing to mechanistic or probabilistic knowledge.

Double Prevention, Causal Judgments, and Counterfactuals.

Mike accidentally knocked against a bottle. Seeing that the bottle was about to fall, Jack was just about to catch it when Peter accidentally knocked against him, making Jack unable to catch it. Jack did not grab the bottle, and it fell to the ground and spilled. In double-prevention cases like these, philosophers and nonphilosophers alike tend to judge that Mike knocking into the bottle caused the beer to spill and that Peter knocking into Jack did not cause the beer to spill. This difference in causal judgment is a difficult puzzle for counterfactual theories of causal judgment; if each event had not happened, the outcome would not have, yet there is a difference in people's causal judgments. In four experiments and three supplemental experiments, we confirm this difference in causal judgments. We also show that differences in people's counterfactual thinking can explain this difference in their causal judgments and that recent counterfactual models of causal judgment can account for these patterns. We discuss these results in relation to work on counterfactual thinking and causal modeling.

Reasoning strategies and prior knowledge effects in contingency learning.

Prior knowledge has been shown to be an important factor in causal judgments. However, inconsistent patterns have been reported regarding the interaction between prior knowledge and the processing of contingency information. In three studies, we examined the effect of the plausibility of the putative cause on causal judgments, when prior expectations about the rate at which the cause is accompanied by the effect in question are explicitly controlled for. Results clearly show that plausibility has a clear effect that is independent of contingency information and type of task (passive or active). We also examined the role of strategy use as an individual difference in causal judgments. Specifically, the dual-strategy model suggests that people can either use a Statistical or a Counterexample strategy to process information. Across all three studies, results showed that Strategy use has a clear effect on causal judgments that is independent of both plausibility and contingency.

What could have been done? Counterfactual alternatives to negative outcomes generated by religious and secular children.

Recent research has shown that a religious upbringing renders children receptive to ordinarily impossible outcomes, but the underlying mechanism for this effect remains unclear. Exposure to religious teachings might alter children's basic understanding of causality. Alternatively, religious exposure might only affect children's religious cognition, not their causal judgments more generally. To test between these possibilities, 6- to 11-year-old children attending either secular (n = 49, 51% female, primarily White and middle-class) or parochial schools (n = 42, 48% female, primarily White and middle-class) heard stories in which characters experienced negative outcomes and indicated how those characters could have prevented them. Both groups of children spontaneously invoked interventions consistent with natural causal laws. Similarly, when judging the plausibility of several counterfactual interventions, participants endorsed the intervention consistent with natural laws at high levels, irrespective of schooling. However, children's endorsement of supernatural interventions inconsistent with these laws revealed both group similarities and differences. Although both groups of children judged divine intervention (i.e., via prayer) as more plausible than mental (i.e., via wishing) and magical (i.e., via magical powers) interventions, children receiving religious (vs. secular) schooling were more likely to do so. Moreover, although children with a secular upbringing overwhelmingly chose naturalistic interventions as the most effective, children with a religious upbringing chose divine as well as naturalistic intervention. These results indicate that religious teaching does not alter children's basic understanding of causality but rather adds divine intervention to their repertoire of possible causal factors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal Judgment in the Wild: Evidence from the 2020 U.S. Presidential Election.

When explaining why an event occurred, people intuitively highlight some causes while ignoring others. How do people decide which causes to select? Models of causal judgment have been evaluated in simple and controlled laboratory experiments, but they have yet to be tested in a complex real-world setting. Here, we provide such a test, in the context of the 2020 U.S. presidential election. Across tens of thousands of simulations of possible election outcomes, we computed, for each state, an adjusted measure of the correlation between a Biden victory in that state and a Biden election victory. These effect size measures accurately predicted the extent to which U.S. participants (N = 207, preregistered) viewed victory in a given state as having caused Biden to win the presidency. Our findings support the theory that people intuitively select as causes of an outcome the factors with the largest standardized causal effect on that outcome across possible counterfactual worlds.

Confidence and gradation in causal judgment

When comparing the roles of the lightning strike and the dry climate in causing the forest fire, one might think that the lightning strike is more of a cause than the dry climate, or one might think that the lightning strike completely caused the fire while the dry conditions did not cause it at all. Psychologists and philosophers have long debated whether such causal judgments are graded; that is, whether people treat some causes as stronger than others. To address this debate, we first reanalyzed data from four recent studies. We found that causal judgments were actually multimodal: although most causal judgments made on a continuous scale were categorical, there was also some gradation. We then tested two competing explanations for this gradation: the confidence explanation, which states that people make graded causal judgments because they have varying degrees of belief in causal relations, and the strength explanation, which states that people make graded causal judgments because they believe that causation itself is graded. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was indeed moderated by confidence: people tended to make graded causal judgments when they were unconfident, but they tended to make more categorical causal judgments when they were confident. Experiment 2 tested the causal strength explanation and showed that although confidence still explained variation in causal judgments, it did not explain away the effects of normality, causal structure, or the number of candidate causes. Overall, we found that causal judgments were multimodal and that people make graded judgments both when they think a cause is weak and when they are uncertain about its causal role.

Challenges to reasoning in forensic science decisions

The success of forensic science depends heavily on human reasoning abilities. Although we typically navigate our lives well using those abilities, decades of psychological science research shows that human reasoning is not always rational. In addition, forensic science often demands that its practitioners reason in non-natural ways. This article addresses how characteristics of human reasoning (either specific to an individual or in general) and characteristics of situations (either specific to a case or in general in a lab) can contribute to errors before, during, or after forensic analyses. In feature comparison judgments, such as fingerprints or firearms, a main challenge is to avoid biases from extraneous knowledge or arising from the comparison method itself. In causal and process judgments, for example fire scenes or pathology, a main challenge is to keep multiple potential hypotheses open as the investigation continues. Considering the contributions to forensic science judgments by persons, situations, and their interaction, reveals ways to develop procedures to decrease errors and improve accuracy.

Conditionals and the hierarchy of causal queries.

Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent. According to Pearl (2009), understanding a causal relation involves multiple, hierarchically organized conceptual dimensions: prediction, intervention, and counterfactual dependence. In a series of experiments, we test the hypothesis that these conceptual dimensions are differentially encoded in indicative and counterfactual conditionals. If this hypothesis holds, then there are limits as to how much of a causal relation is captured by indicative conditionals alone. Our results show that the acceptance of indicative and counterfactual conditionals can become dissociated. Furthermore, it is found that the acceptance of both is needed for accepting a causal relation between two co-occurring events. The implications that these findings have for the hypothesis above, and for recent debates at the intersection of the psychology of reasoning and causal judgment, are critically discussed. Our findings are consistent with viewing indicative conditionals as answering predictive queries requiring evidential relevance (even in the absence of direct causal relations). Counterfactual conditionals in contrast target causal relevance, specifically. Finally, we discuss the implications our results have for the yet unsolved question of how reasoners succeed in constructing causal models from verbal descriptions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal invariance as a tacit aspiration: Analytic knowledge of invariance functions

For causal knowledge to be worth learning, it must remain valid when that knowledge is applied. Because unknown background causes are potentially present, and may vary across the learning and application contexts, extricating the strength of a candidate cause requires an assumption regarding the decomposition of the observed outcome into the unobservable influences from the candidate and from background causes. Acquiring stable, useable causal knowledge is challenging when the search space of candidate causes is large, such that the reasoner’s current set of candidates may fail to include a cause that generalizes well to an application context. We have hypothesized that an indispensable navigation device that shapes our causal representations toward useable knowledge involves the concept of causal invariance – the sameness of how a cause operates to produce an effect across contexts. Here, we tested our causal invariance hypothesis by making use of the distinct mathematical functions expressing causal invariance for two outcome-variable types: continuous and binary. Our hypothesis predicts that, given identical prior domain knowledge, intuitive causal judgments should vary in accord with the causal-invariance function for a reasoner’s perceived outcome-variable type. The judgments are made as if the reasoner aspires to formulate causally invariant knowledge. Our experiments involved two cue-competition paradigms: blocking and overexpectation. Results show that adult humans tacitly use the appropriate causal-invariance functions for decomposition. Our analysis offers an explanation for the apparent elusiveness of the blocking effect and the adaptiveness of intuitive causal inference to the representation-dependent reality in the mind.

The role of mechanism knowledge in singular causation judgments

Singular causation queries (e.g., “Did Mary's taking contraceptives cause her thrombosis?”) are ubiquitous in everyday life and crucial in many professional disciplines, such as medicine or law. Knowledge about general causal regularities is necessary but not sufficient for establishing a singular causation relation because it is possible that co-occurrences consistent with known regularities are in an individual case still just coincidental. Thus, further cues are helpful to establish a singular causation relation. In the present research we focus on information about mechanisms as a potent cue. While previous studies have shown that reasoners consider mechanism information as important when it comes to answering singular causation queries, no formal model has been proposed that explains why this is case. We here present a computational model that explains how causal mechanism information affects singular causation judgments. We also use the model to identify conditions that restrict the utility of mechanism information. We report three experiments testing the implications of our formal analysis. In Experiment 1 we found that reasoners systematically use mechanism information, largely in accordance with our formal model, although we also discovered that some people seem to rely on simpler, computationally less demanding reasoning strategies. The results of Experiments 2 and 3 demonstrate that reasoners have a tentative understanding of the conditions that restrict the utility of causal mechanism information.

Debiasing Causal Inferences: Over and Beyond Suboptimal Sampling

Background We have previously presented two educational interventions aimed to diminish causal illusions and promote critical thinking. In both cases, these interventions reduced causal illusions developed in response to active contingency learning tasks, in which participants were able to decide whether to introduce the potential cause in each of the learning trials. The reduction of causal judgments appeared to be influenced by differences in the frequency with which the participants decided to apply the potential cause, hence indicating that the intervention affected their information sampling strategies. Objective In the present study, we investigated whether one of these interventions also reduces causal illusions when covariation information is acquired passively. Method Forty-one psychology undergraduates received our debiasing intervention, while 31 students were assigned to a control condition. All participants completed a passive contingency learning task. Results We found weaker causal illusions in students that participated in the debiasing intervention, compared to the control group. Conclusion The intervention affects not only the way the participants look for new evidence, but also the way they interpret given information. Teaching implications Our data extending previous results regarding evidence-based educational interventions aimed to promote critical thinking to situations in which we act as mere observers.

A counterfactual simulation model of causal judgments for physical events.

How do people make causal judgments about physical events? We introduce the counterfactual simulation model (CSM) which predicts causal judgments in physical settings by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in several experiments in which participants make causal judgments about dynamic collision events. A preliminary study establishes a very close quantitative mapping between causal and counterfactual judgments. Experiment 1 demonstrates that counterfactuals are necessary for explaining causal judgments. Participants' judgments differed dramatically between pairs of situations in which what actually happened was identical, but where what would have happened differed. Experiment 2 features multiple candidate causes and shows that participants' judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants' judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A counterfactual simulation model of causation by omission

When do people say that an event that did not happen was a cause? We extend the counterfactual simulation model (CSM) of causal judgment (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2021) and test it in a series of three experiments that look at people's causal judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of questions that need to be answered in order to give an adequate causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative causal relationships evaluated, and how is the causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make causal judgments about omissions in physical interactions by using their intuitive understanding of physics to mentally simulate what would have happened in relevant counterfactual situations. Prior work has argued that normative expectations affect judgments of omissive causation. Here we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's judgments.