Counterfactuals

By D. Makinson. 1974

Subfields: Causal Inference & Discovery, Philosophy

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Citations in the corpus

Counterfactual Fairness (2017, background): “Counterfactuals and similarities are related, as in the classical notion of distances between “worlds” corresponding to diﬀerent counterfactuals [20].”
Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning (2019, background): “Their relevance to a variety of AI applications has been known for some time, ranging from subgoal construction to the identification of planning failures, from fault diagnosis to the determination of liability [Ginsberg, 1986; Halpern and Pearl, 2005].”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “[50, 65]), I focus on the model of Chockler and Halpern [16] because, as far I am aware aware, experimental evaluation of the model shows it to be stronger than existing models [27], and because it is a formal model that is more readily adopted in”
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “A popular approach to this task is to ﬁnd so-called (nearest) counterfactual explanations [47], where the term “counterfactual” is meant in the sense of the closest possible world with a different outcome [22].”
Invariant Risk Minimization (2019, background): “This is in the spirit of modal realism and possible worlds [26], where we could consider, for instance, environments where we switch oﬀ the Sun.”
Explaining Explanations in AI (2018, neutral): “In recent decades, work in the philosophy of science and epis-temology has paid increasing attention to theories of contrastive explanations and counterfactual causality (e.g. Kment (2006); Lewis (1973); Ruben (2004); Woodward and Zalta (2003)).”
Efficient Search for Diverse Coherent Explanations (2019, background): “Another promising approach to explaining the incomprehensible models of machine learning lies in counterfactual explanations (Lewis, 1973; Wachter et al., 2018).”
On Pearl’s Hierarchy and the Foundations of Causal Inference (2022, background): “In David Lewis’s early work on counterfactual conditionals, Y X = 1 = 1 would have been written X = 1 □→ Y = 1 (see Lewis [1973]).”
Anankastic conditionals are just conditionals (2016, background): “This means that g should be such that adding any goal proposition to g(w 0 ) via the antecedent results in a g + (w 0 ) that can non-spuriously verify the consequent of the conditional.”
Dynamic logic for belief revision (2007, background): “(Lewis, 1973) also imposes connectedness : worlds either precede each other, or they have the same predecessors and successors.”
Open Set Learning with Counterfactual Images (2018, background): “A counterfactual conditional, p →q is a conditional statement in which p is known to be false [24].”
Rethinking Superdeterminism (2019, background): “This indeed is the basis of Lewis’s celebrated theory of causality by counterfactuals: of two possible counterfactual worlds, the one that resembles reality more closely is presumed closer to reality [37].”
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference (2007, background): “3.1.1), but key aspects of the ideas originate with many others, especially Neyman (1923), Fisher (1935), Cox (1958), Rubin (1974), and Holland (1986) in statistics; Roy (1951) and Quandt (1972) in econometrics; and Lewis (1973) in philosophy.”