Causal selection – the linguistic take

Causal Selection is a widely discussed topic in philosophy and the cognitive sciences, concerned with characterizing the choice of "the cause" among the many individually necessary and jointly sufficient conditions on which any effect depends on. In this paper, we argue for an additional selection process underlying causal statements: Causative-Construction Selection, which pertains to the choice of linguistic constructions used to express causal relations. By exploring this phenomenon, we aim to answer the following question: given that a speaker wishes to describe the relation between one of the conditions and the effect, which linguistic constructions are available? We take CC-selection to be more crucial than causal selection, since the latter is in fact restricted by the linguistic options resulting from the former. Based on a series of experiments, we demonstrate that factors taken previously as contributing to causal selection should, in fact, be considered as the parameters that license the various linguistic constructions under given circumstances, based on previous knowledge about the causal structure of the world (the causal model). These factors are therefore part of the meaning of the causative expressions.

How Pills Undermine Skills: Moralization of Cognitive Enhancement and Causal Selection

Despite the promise to boost human potential and wellbeing, enhancement drugs face recurring ethical scrutiny. The present studies examined attitudes toward cognitive enhancement in order to learn more about these ethical concerns, who has them, and the circumstances in which they arise. Fairness-based concerns underlay opposition to competitive use—even though enhancement drugs were described as legal, accessible and affordable. Moral values also influenced how subsequent rewards were causally explained: Opposition to competitive use reduced the causal contribution of the enhanced winner’s skill, particularly among fairness-minded individuals. In a follow-up study, we asked: Would the normalization of cognitive enhancement alleviate concerns about its unfairness? Indeed, proliferation of competitive cognitive enhancement eradicated fairness-based concerns, and boosted the causal role of the winner’s skill. In contrast, purity-based concerns emerged in both recreational and competitive contexts, and were not assuaged by normalization.

When do we think that X caused Y?

When explaining an event, people tend to select a single cause out of the multiple factors that contributed -- for instance, they will say that a forest fire was caused by a lit match, without mentioning the oxygen in the air which helped fuel the fire. Recently scholars have suggested that causal selection is designed to provide explanations that are likely to generalize across a variety of background circumstances. Here, we develop a computational model of causal selection which formalizes this idea. Under minimal assumptions, the model is surprisingly simple: a factor is regarded as a cause of an outcome to the extent that it is, across counterfactual worlds, correlated with that outcome. The model explains why causal selection is influenced by the normality of candidate causes, and outperforms other known computational models when tested against a fine-grained dataset of human graded causal judgments.

Quantitative causal selection patterns in token causation

When many events contributed to an outcome, people consistently judge some more causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air -- in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people's causal judgments are on a continuum -- and relatively little is known about how these judgments vary quantitatively as the prior probabilities change. In this paper, we measure people's causal judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories.