An interaction effect of norm violations on causal judgment

Existing research has shown that norm violations influence causal judgments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interaction 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.

Proportionality

This chapter discusses, again from both a normative and a descriptive perspective, issues having to do with the role of proportionality in causal judgment. In my treatment, proportionality has to do, roughly, with the extent to which a cause is characterized in such a way that the variation in the effect is captured by variation in the cause. Proportionality was introduced into philosophical discussion by Yablo; this chapter retains the underlying idea of his proposal but reformulates it in order to respond to various philosophical criticisms. It is argued that, so understood, proportionality has a natural normative rationale and that there is experimental evidence that ordinary subjects judge in accord with it. Several different formulations of proportionality are explored and contrasted.

Thinking Structurally: A Cognitive Framework for Understanding How People Infer Structural Causes of Inequality

To accurately explain social group inequalities, people must consider structural explanations, which are causal explanations that appeal to societal factors such as discriminatory institutions and policies. Structural explanations are a distinct type of extrinsic explanation—they identify stable societal forces that are experienced by specific social groups. We argue that a novel framework is needed to specify how people infer structural causes of inequality. The proposed framework is rooted in counterfactual theories of causal judgment, positing that people infer structural causes by discerning whether structural factors were “difference-making” for the inequality they observe. Building on this foundation, our framework makes the following novel contributions: First, we propose specific types of evidence that support this inference, and second, we consider the unique contextual, cognitive, and motivational barriers to the availability and acceptance of this evidence. We conclude by exploring how the framework might be applied in future research examining people’s explanations for inequality.

Certainty and Singular Causal Judgment

In this paper, I map out broad aims, challenges, predictions, and implica- tions for the resulting intersection of singular causal judgment and metacognition that I (tentatively) call causal metacognition. First, I will overview research on sin- gular causal judgment, focusing on popular counterfactual theories that provide a formal framework for evaluating dependency relationships, as well as several compet- ing definitions of singular causal strength. Next, I will provide relevant background in the literature on metacognition for perception and decision-making, discussing major computational theories of metacognitive judgments. After covering the small amount of work on uncertainty in causal judgments, I will then argue that although singular causal judgments pose a particular problem for some theories of metacognition, coun- terfactual theories of singular causal judgment already provide testable predictions for confidence in causal judgments and can be extended to account for a wide range of patterns in confidence in singular causal judgments. Finally, I will summarize why we need a study of causal metacognition, and what empirical and theoretical advancements in that field might look like.

If it works we didn't need it: Intuitive judgments of 'overreaction'

When laypeople decide if a costly intervention is an overreaction or an appropriate response, they likely base those judgments on mental simulation about what could happen, or what would have happened without an intervention. To narrow down from the infinite set of possibilities they could consider, they may engage in a process of sampling. We examine whether judgments of overreaction can be explained by a utility- weighted sampling account from the JDM literature, or a norm- weighted sampling account from the causal judgment literature, both, or neither. Three experiments test whether these judgments are overly influenced by low-risk bad outcomes (utility-weighted sampling), or by what is likely and prescriptively good (norm-weighted sampling). Overall, participants’ judgments indicate that they disregard low-risk bad outcomes, and even when a high-risk outcome is successfully avoided, the intervention is an overreaction. These results favor a norm-weighted sampling account in the specific case of evaluating overreactions.

Causal judgment in the wild: evidence from the 2020 US presidential election

A given event has many causes, but people intuitively view some causes as more important than others. Models of causal judgment have been evaluated in controlled laboratory experiments, but they have yet to be tested in complex real-world settings. 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, pre-registered) viewed victory in a given state as having caused Biden to win the presidency. This supports the theory that people intuitively select as causes of an outcome the factors with the largest average causal effect on that outcome across possible counterfactual worlds.

Counterfactual Thinking and Recency Effects in Causal Judgment

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (N = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of the extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one (Experiment 2). Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens (and sometimes eliminates) the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure (Experiments 3 & 5). We discuss these results in relation to work on counterfactual thinking and causal modeling.

Inference from explanation

What do we communicate with causal explanations? Upon being told, "E because C", one 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.