Causal scope, the number of different effects a cause can pro-duce, is a salient feature of causes. In the present research, weaddress the question whether reasoners use causal scope as adiagnostic cue to infer the strengths of individual causal links.In three experiments, we manipulated the number of effects ofa cause, and asked subjects to assess the causal strengths ofsingle causal links. The results document a clear influence ofcausal scope on perceived link strength. In particular, subjectstended to display a “dilution” effect. They perceived a causallink to be weaker if that link belonged to a cause that is capa-ble of producing additional effects. This dilution effect can beexplained by a dispositional notion of causality according towhich a cause possesses a certain amount of causal power orcapacity that it distributes across its different causal pathways.

Experiments have shown that prescriptive norms often influence causal inferences. The reason for this effect is still not clear. One problem of the studies is that the term ‘cause’ in the test questions is ambiguous and can refer to both the causal mechanism and the agent’s accountability. Possibly subjects interpreted the causal test question as a request to assess accountability rather than causality. Scenarios that put more stress on the causal mechanism should therefore yield no norm effect. Consequently, Experiment 1 demonstrates that norms no longer influence causal judgments when the causal information is presented in a trial-by-trial learning task. Furthermore, Experiment 2 shows that norm effects are only obtained when the test question asks about a (potentially accountable) person but not when asked about a component of the causal mechanism. Both findings demonstrate that norms cease to influence causal judgments when the task settings highlight causal relations

Many actions have both an intended primary effect and unin-tended, but foreseen side effects. In two experiments we inves-tigated how people morally evaluate such situations. While anegative side effect was held constant across conditions in Ex-periment 1, we varied features of the positive primary effect.We found that judgments of moral justification of actions weresensitive to the numerical ratios of helped versus harmed enti-ties as well as to the kind of state change that was induced byan agent’s action (saving entities from harm versus improvingtheir status quo). Judgments of moral responsibility for sideeffects were only sensitive to the latter manipulation. In Ex-periment 2, we found initial support for a subjective utilitarianexplanation of the moral justification judgments.

Queries about singular causation face two problems: It needsto be decided whether the two observed events are instanti-ations of a generic cause-effect relation. Second, causationneeds to be distinguished from coincidence. We propose acomputational model that addresses both questions. It accessesgeneric causal knowledge either on the individual or the grouplevel. Moreover, the model considers the possibility of a co-incidence by adopting Cheng and Novick’s (2005) power PCmeasure of causal responsibility. This measure delivers theconditional probability that a cause is causally responsible foran effect given that both events have occurred. To take uncer-tainty about both the causal structure and the parameters intoaccount we embedded the causal responsibility measure withinthe structure induction (SI) model developed by Meder et al.(2014). We report the results of three experiments that showthat the SI model better captures the data than the power PCmodel.

Causal queries about singular cases are ubiquitous, yet thequestion of how we assess whether a particular outcome wasactually caused by a specific potential cause turns out to bedifficult to answer. Relying on the causal power approach,Cheng and Novick (2005) proposed a model of causal attribu-tion intended to help answering this question. We challengethis model, both conceptually and empirically. The centralproblem of this model is that it treats the presence of sufficientcauses as necessarily causal in singular causation, and thus ne-glects that causes can be preempted in their efficacy. Also, themodel does not take into account that reasoners incorporateuncertainty about the underlying causal structure and strengthof causes when making causal inferences. We propose a newmeasure of causal attribution and embed it into our structure in-duction model of singular causation (SISC). Two experimentssupport the model.

Many philosophers and psychologists argue that causal inferences are solely based on the observation of contingencies between potential causes and effects. By contrast, causal-model theory postulates that the interpretation of the learning input is governed by prior causal assumptions. Simpson's paradox is an example of this basic claim of causal-model theory. Identical observations may result in dramatically different causal impressions depending on the partitioning of the event space. Tw o experiments are presented that show that participants' assessment of a contingency between a potential cause and an effect is moderated by their background assumptions about the causal relevance of additional variables, and the ordering of the learning items. Despite the fact that all participants received identical learning inputs, participants' assumptions about the causal relevance of an additional grouping variable led either to the impression that the cause facilitated the effect or to an impression that it prevented the effect. Thus, the acquisition of new causal knowledge is crucially dependent on causal knowledge that is already available at the outset of the induction process.