Causal Responsibility and Counterfactuals

By David A. Lagnado, Tobias Gerstenberg, Ro’i Zultan. 2013

207 citations recorded by the source corpus.

How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions.

Subfields: Cognitive Neuroscience, Philosophy

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Citations in the corpus

Causal Reasoning from Meta-reinforcement Learning (2019, background): “Causal reasoning may be an essential component of natural ∗ Corresponding author: ishitadasgupta@g.harvard.edu intelligence and is present in human babies, rats, and even birds (Leslie, 1982; Gopnik et al., 2001; 2004; Blaisdell et al., 2006; Lagnado et al., 2013).”
Causal superseding. (2015, background): “For example, previous work has shown that people’s causal and responsibility attributions are reduced in situations in which the outcome was causally overdetermined by multiple individually sufficient causes (Gerstenberg & Lagnado, 2010, 2012; Lagnado et al., 2013; Zultan et al., 2012).”
Algorithmic Recourse: from Counterfactual Explanations to Interventions (2020, background): “4Queries such as this subsume both retrospective/subjunctive/counterfactual (“what would have been the value of”) and prospective/indicative/predictive (“what will be the value of”) conditionals [11, 25, 48], as long as we assume that the laws governing the world, F, are stationary.”
Counterfactual and semi-factual thoughts in moral judgements about failed attempts to harm (2017, background): “…explanation for the difference between the counterfactual and semi-factual conditions in the previous experiments is the protagonist’s “pivotality”: protagonists are judged to be more causally responsible for an outcome if it could not have occurred without their input (e.g., Lagnado et al., 2013).”
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2023, background): “2020; Wong et al., 2023), and that humans can inter-vene on these models to perform rapid and flexible counterfactual simulations (Lagnado et al., 2013; Gerstenberg et al., 2017, 2021).”