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

Promoting climate actions: A cognitive-constraints approach

The present paper reports an experiment with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take political and personal climate actions. Many Americans still do not view climate change as a threat requiring urgent action. Moreover, among American conservatives, higher science literacy is paradoxically associated with higher anthropogenic climate-change skepticism. Our experimental materials were designed to harness the power of two central cognitive constraints - coherence and causal invariance, which map onto two narrative proclivities that anthropologists have identified as universal - to promote climate action across the political spectrum. Towards that goal, the essential role of these constraints in the causal-belief-formation process predicts that climate-change information would be more persuasive when it is embedded in a personal climate-action narrative, the evocation of which can benefit from exposure to parsimonious scientific explanations of indisputable everyday observations, juxtaposed with reasoners' own, typically less coherent explanations, occurring in a context that engages their moral stance. Our brief one-time intervention, conducted in ten U.S. states with the highest level of climate skepticism, showed that across the political spectrum, our materials raised appreciation of science, openness to alternative views, and willingness to take climate actions in the immediate assessment. It also raised how likely were reports two years later of having taken those actions or would have taken them had the opportunity existed, suggesting a long-lasting effect. Our approach adopts the framework that conceptions of reality are representations, and adaptive solutions in that infinite space of representations require cognitive constraints to narrow the search.

The perceived dilution of causal strength

Dependency theories of causal reasoning, such as causal Bayes net accounts, postulate that the strengths of individual causal links are independent of the causal structure in which they are embedded; they are inferred from dependency information, such as statistical regularities. We propose a psychological account that postulates that reasoners' concept of causality is richer. It predicts a systematic influence of causal structure knowledge on causal strength intuitions. Our view incorporates the notion held by dispositional theories that causes produce effects in virtue of an underlying causal capacity. Going beyond existing normative dispositional theories, however, we argue that reasoners' concept of causality involves the idea that continuous causes spread their capacity across their different causal pathways, analogous to fluids running through pipe systems. Such a representation leads to the prediction of a structure-dependent dilution of causal strength: the more links are served by a cause, the weaker individual links are expected to be. A series of experiments corroborate the theory. For continuous causes with continuous effects, but not in causal structures with genuinely binary variables that can only be present or absent, reasoners tend to think that link strength decreases with the number of links served by a cause. The effect reflects a default notion reasoners have about causality, but it is moderated by assumptions about the amount of causal capacity causes are assumed to possess, and by mechanism knowledge about how a cause generates its effect(s). We discuss the theoretical and empirical implications of our findings.

How do humans want causes to combine their effects? The role of analytically-defined causal invariance for generalizable causal knowledge

The present paper reports two experiments (N = 232, 254) addressing the question: How do reasoners reconcile the desire to have useable (i.e., invariant) causal knowledge - knowledge that holds true when applied in new circumstances/contexts - with the reality that causes often interact with other causes present in the context? The experiments test two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners' ability to learn rules (functions) describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function-learning view predicts that participants would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one's current representation may not yield useable causal knowledge. This view posits that the human causal-induction process incorporates invariant knowledge as an aspiration, entailing that observed deviation from causal invariance when the knowledge is applied serves as a signal for a need to revise causal knowledge: Only invariance across contexts with potentially new causal factors justifies generalization across them. The invariance view therefore predicts that reasoners would revise their representation so that they have whole causes - potentially consisting of interacting components - that do not interact with each other, even when in their relevant experience all (pre-defined) causes interact. Across both experiments, our results favor the causal-invariance view: Participants generalize their empirically learned function (which may involve interactions) to new stimuli, but switch to the analytic causal-invariance function for both old and new stimuli at the level of the whole cause, indicating that how humans want causes to combine their effects shapes the knowledge they induce.

Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Pre-schoolers

The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of causal-invariance decomposition functions is essential for this process. Such knowledge specifies the decomposition of an observed outcome into contributions from constituent causes under the default assumption that the empirical knowledge acquired is invariant across contextual/background causes. The paper reports two psychological experiments (and replication studies) with pre-school-age children on generalization across contexts involving binary cause and effect variables. The critical role of causal invariance for constructing useable causal knowledge predicts that even young children should (tacitly) use the causal-invariance decomposition function for such variables rather than a non-causal-invariance decomposition function common in statistical practice in research involving binary outcomes. The findings support the rational shaping of empirical causal knowledge by the causal-invariance constraint, ruling out alternative explanations in terms of non-causal-invariance decomposition functions, heuristics, and biases. For the same causal structure involving candidate causes and outcomes that are binary variables with a "present" value and an "absent" value, the paper argues against the possibility of multiple rational characterizations of the "sameness of causal influence" that justifies generalization across contexts.