Causation, norms, and omissions: A study of causal judgments

Clarke, Randolph; Shepherd, Joshua; Stigall, John; Waller, Robyn Repko; Zarpentine, Chris

doi:10.1080/09515089.2013.815099

Cited by 19 publications

(15 citation statements)

References 11 publications

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“…While it was the gardener's duty to water the plants, and he was expected to do so, the Queen of England was not responsible for the plants. Several empirical studies have confirmed the role that norms play for judgments about omissions (Clarke, Shepherd, Stigall, Waller, & Zarpentine, 2015;Henne, Pinillos, & De Brigard, 2016;Livengood & Machery, 2007). For example, when two cars collide at an intersection whereby one driver had a red light and the other one a green light, participants judge the not stepping on the brakes of the driver with the red light to be more causal (Clarke et al, 2015).…”

Section: Future Directions and Open Challengesmentioning

confidence: 94%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

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How do people make causal judgments? We propose the counterfactual simulation model (CSM) of causal judgment which predicts causal judgments by comparing what actually happened with counterfactual simulations of what would have happened in relevant contingencies. It postulates different aspects of causation that capture whether the cause made a difference to whether and how the outcome occurred, and if the cause was sufficient and robust. We test the CSM in three experiments that ask participants to make judgments about dynamic collision events. Experiment 1 reveals a very close quantitative mapping between causal judgments, and participants’ belief that the outcome would have been different without the cause. Experiment 2 demonstrates that counterfactual contrasts are necessary for explaining causal judgments. Participants’ judgments differ dramatically between pairs of situations in which what actually happened was identical, but what would have happened differed. Experiment 3 features two candidate causes and shows that participants’ judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants’ judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, deal with causation by omission, and be extended beyond the physical domain.

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Section: Future Directions and Open Challengesmentioning

confidence: 94%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…19 For theoretical discussions of causation by omission, see Schaffer (2000); Beebee (2004); Mellor (2004); Lewis (2004);McGrath (2005); Moore (2009); Bernstein (2014). For empirical work, see Livengood and Machery (2007); Wolff, Barbey, and Hausknecht (2010); Clarke et al (2015); Henne, Pinillos and De Brigard (2017); Bello et al (2017); Stephan et al (2017); Khemlani et al (2018); Willemsen (2019).…”

Section: Acts Versus Omissionsmentioning

confidence: 99%

Causal attributions and the trolley problem

Sytsma

Livengood

2021

Philosophical Psychology

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In this paper, we consider two competing explanations of the empirical finding that people's causal attributions are responsive to normative details, such as whether an agent's action violated an injunctive norm-the counterfactual view and the responsibility view. We then present experimental evidence that uses the trolley dilemma in a new way to investigate causal attribution. In the switch version of the trolley problem, people judge that the agent ought to flip the switch, but they also judge that she is more responsible for the resulting outcome when she does so than when she refrains. As predicted by the responsibility view, but not the counterfactual view, people are more likely to say that the agent caused the outcome when she flips the switch.

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“…Dependency theorists therefore have the burden of explaining how omissive causation applies to relevant agents in a particular scenario, rather than to the many other agents that might have otherwise impacted the outcome under discussion (McGrath, 2005). Following suggestions originally made by Hart and Honoré (1985), recent accounts propose that norms, typicality, and default assumptions determine the most intuitive cause of an outcome with multiple putative causes (e.g., Clarke, Shepherd, Stigall, Waller, & Zarpentine, 2013;Hall, 2007;Halpern & Hitchcock, 2014;Henne, Pinillos, & De Brigard, 2017;Menzies, 2004Menzies, , 2007.…”

Section: Omissive Causation In Philosophy and Psychologymentioning

confidence: 99%

Mental models and omissive causation

et al. 2018

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Some causal relations refer to causation by commission (e.g., "A gunshot causes death"), and others refer to causation by omission (e.g., "Not breathing causes death"). We describe a theory of the representation of omissive causation based on the assumption that people mentally simulate sets of possibilities-mental models-that represent causes, enabling conditions, and preventions (Goldvarg & Johnson-Laird, 2001). The theory holds that omissive causes, enabling conditions, and preventions each refer to distinct sets of possibilities. For any such causal relation, reasoners typically simulate one initial possibility, but they are able to consider alternative possibilities through deliberation. These alternative possibilities allow them to deliberate over finer-grained distinctions when reasoning about causes and effects. Hence, reasoners should be able to distinguish between omissive causes and omissive enabling conditions. Four experiments corroborated the predictions of the theory. We describe them and contrast the results with the predictions of alternative accounts of causal representation and inference.

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Causation, norms, and omissions: A study of causal judgments

Cited by 19 publications

References 11 publications

A counterfactual simulation model of causal judgments for physical events

A counterfactual simulation model of causal judgments for physical events

Causal attributions and the trolley problem

Mental models and omissive causation

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