Inference from explanation.

Abstract

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.

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

“…But this effect reverses when either factor would have been sufficient to produce the outcome. This pattern of effects has been replicated many times across a large range of stimuli (Icard et al., 2017; Gerstenberg & Icard, 2020; Kominsky & Phillips, 2019; Quillien & German, 2021; O'Neill et al., 2021; Kirfel et al., in press; see also Henne et al., 2019, 2021b).…”

What would have happened? Counterfactuals, hypotheticals and causal judgements

“…Accordingly, certain counterfactuals come to mind more easily than others, and this affects what events are selected as causes [81]. Another related view suggests that people select those events as causes that would make for good points of intervention [58,[82][83][84]. While these theoretical accounts lead to similar predictions in many instances, they come apart in scenarios like the one presented here.…”

“…The simulation model predicts that uncertainty about the block's movement should only affect hypothetical judgements but not the counterfactual, or causal judgements. That said, much work has shown that the (ab)normality of events influences causal judgements [72][73][74][75][76], and it is possible that such effects would be observed in this setting too (see [77,83]). For example, consider a situation in which the block is initially out of the way but then moves in front of the gate.…”

The logic of guesses: how people communicate probabilistic information

“…For example, people can transmit a lot of statistical information about the properties of a category by using generics, such as 'robins lay eggs' (Tessler & Goodman, 2019). They are also able to communicate the content of their complex causal models of the world, by making short statements about what could have been (Lucas & Kemp, 2015), or highlighting one cause among the many factors that contributed to an event (Kirfel et al, 2021;Quillien, 2020). Here, we suggested that people can also also efficiently express their probabilistic beliefs about the world in the form of simple guesses.…”