From counterfactual simulation to causal judgment

By Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado, Josh Tenenbaum. 2014

47 citations recorded by the source corpus.

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Citations in the corpus

Visual Interaction Networks: Learning a Physics Simulator from Video (2017, background): “Overcoming this challenge will help close the gap between human and machine performance on important classes of behavior that depend on physical reasoning, such as model-based decision-making [3], physical inference [13], and counterfactual reasoning [10, 11].”
How, whether, why: Causal judgments as counterfactual contrasts (2015, background): “In previous work, we had shown that participants’ counterfactual simulations were accurate for simple cases with two balls (Gerstenberg et al., 2012), and situations that involved additional objects such as bricks or teleports (Gerstenberg et al., 2014).”
Inferring mass in complex scenes by mental simulation. (2016, unknown): “Indeed, recent work by Ullman et al. (2014) and Gerstenberg et al. (2012, 2014) has provided examples of how simulations might be used in simple 2D scenes to estimate the necessary probabilities for Bayesian inference .”
Causal superseding. (2015, background): “While we do believe that causal judgments are closely linked to counterfactual simulations over causal representations (cf. Chater & Oaksford, 2013; Gerstenberg et al., 2014; Goodman, Tenenbaum, & Gerstenberg, in press), we have also made it clear that the proposed account of the superseding effect…”
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality (2023, background): “Judgement tasks (Gerstenberg et al., 2014) extend attribution tasks to questions of reward or blame assignment for outcomes.”
Eye-Tracking Causality (2017, background): “and we have shown in other work that this kind of simulation forms a necessary component of people’s causal judgments (Gerstenberg et al., 2014).”