Time and Singular Causation—A Computational Model

Abstract

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model.

Evaluating General versus Singular Causal Prevention

“…We now show how, under the above conditions, p c (or V E) can be used to estimate the probability with which a preventive cause has actually prevented the effect in a singular case. To accomplish this, we build on the generalized power PC model of singular causation judgments (Stephan et al, 2020;Stephan & Waldmann, 2018), which models singular causation judgments in generative causal scenarios. In the generative case, in which C is assumed to produce E with strength q c , the model provides answers to queries like "How likely is it that C caused E in this particular case in which c + and e + actually co-occurred?"…”

“…The parameter α determines the proportion of cases on which this is the case. According to the theory, α can be determined based on temporal information (see Stephan et al, 2020).…”

“…To answer the question of how the presence of a singular instance of prevention can be assessed based on knowledge about the general strength of a preventive cause, we propose a novel equation inspired by the generalized power PC model of singular causation judgments (Stephan, Mayrhofer, & Waldmann, 2020;Stephan & Waldmann, 2018). The model has previously been used to model singular causation judgments in generative cases, but not singular prevention judgments.…”

The interplay between covariation, temporal, and mechanism information in singular causation judgments

“…We recently have begun to investigate how reasoners use their general causation knowledge to answer singular causation queries (Stephan, Mayrhofer, & Waldmann, 2020;Stephan & Waldmann, 2018, under review). Building on the power PC model of causal attribution proposed by (Cheng & Novick, 2005), we have proposed the generalized power PC model of singular causation judgments.…”

“…Building on the power PC model of causal attribution proposed by (Cheng & Novick, 2005), we have proposed the generalized power PC model of singular causation judgments. The model computes the probability that a particular target cause c actually caused an observed target effect e. The model combines information about the general strengths of the potential causes of the target effect, which can be induced based on covariational information, with information about temporal relations between causes and effects (Stephan et al, 2020).…”