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.
Published in Petroleum Transactions, AIME, Volume 207, 1956, pages 215–221. Abstract Experimental studies covering a wide range of core materials and fluid properties have been conducted to determine the mechanism of oil displacement by water in a partially gas-saturated porous medium. In all instances, the presence of a gas phase was found to have a beneficial effect in reducing residual oil saturations. The practical significance of this benefit is discussed, and a simplified procedure is outlined for evaluating the effects of free gas on water flooding by means of short core tests. Introduction In addition to oil and water, reservoirs subjected to water flooding frequently contain, also, a gas phase. Common engineering procedures account for the presence of this "free gas" only from the viewpoint of volumetric balance, implying that the only role of the gas consists of providing "fill up" space. It is usually visualized that during the initial stages of the water invasion, the oil, moving ahead of the water, displaces part of the gas and that subsequently the remaining portion of the gas phase is totally compressed and dissolved in the advancing oil bank. Thus, consideration of a two-phase, water-oil flooding mechanism supplies an adequate basis for predictions of oil recovery if the pressure build-up caused by the flood is sufficiently great, so as to reduce the free gas saturation to a negligible value in any portion of the reservoir by the time that portion is reached by the advancing flood water. In many in stances, however, waterflooding operations are carried out when the reservoir pressure is still relatively high so that the pressure build-up associated with the water flood may not result in complete dissipation of the gas phase. Under such circumstances it is necessary to ascertain whether or not the presence of free gas has an effect on waterflood behavior and, if so, to account for any such effect in the evaluation of field operations.
Causal queries about singular cases are ubiquitous, yet the question of how we assess whether a particular outcome was actually caused by a specific potential cause turns out to be difficult to answer. Relying on the causal power framework (Cheng, 1997), Cheng and Novick (2005) proposed a model of causal attribution intended to help answer this question. We challenge this model, both conceptually and empirically. We argue that the central problem of this model is that it treats causal powers that are probabilistically sufficient to generate the effect on a particular occasion as actual causes of the effect, and thus neglects that sufficient causal powers can be preempted in their efficacy. Also, the model does not take into account that reasoners incorporate uncertainty about the underlying general causal structure and strength of causes when making causal inferences. We propose a new measure of causal attribution and embed it into the structure induction model of singular causation (SISC; Stephan & Waldmann, 2016). Two experiments support the model.
When do people say that an event that didn't happen was a cause? We extend the counterfactual simulation model (CSM) of causal judgment (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2021) and test it in a series of three experiments that look at people's causal judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of questions that need to be answered in order to give an adequate causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative causal relationships evaluated, and how is the causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make causal judgments about omissions in physical interactions by using their intuitive understanding of physics to mentally simulate what would have happened in relevant counterfactual situations. Prior work has argued that normative expectations affect judgments of omissive causation. Here we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's judgments.
Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent.According to Pearl ( 2009), understanding a causal relation involves multiple, hierarchically organized conceptual dimensions: prediction, intervention, and counterfactual dependence. In a series of experiments, we test the hypothesis that these conceptual dimensions are differentially encoded in indicative and counterfactual conditionals. If this hypothesis holds, then there are limits as to how much of a causal relation is captured by indicative conditionals alone. Our results show that the acceptance of indicative and counterfactual conditionals can become dissociated. Furthermore, it is found that the acceptance of both is needed for accepting a causal relation between two co-occurring events. The implications that these findings have for the hypothesis above, and for recent debates at the intersection of the psychology of reasoning and causal judgment, are critically discussed. Our findings are consistent with viewing indicative conditionals as answering predictive queries requiring evidential relevance (even in the absence of direct causal relations). Counterfactual conditionals in contrast target causal relevance, specifically. Finally, we discuss the implications our results have for the yet unsolved question of how reasoners succeed in constructing causal models from verbal descriptions.
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