A counterfactual simulation model of causal judgments for physical events.

Abstract

How do people make causal judgments about physical events? We introduce the counterfactual simulation model (CSM) which predicts causal judgments in physical settings by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in several experiments in which participants make causal judgments about dynamic collision events. A preliminary study establishes a very close quantitative mapping between causal and counterfactual judgments. Experiment 1 demonstrates that counterfactuals are necessary for explaining causal judgments. Participants' judgments differed dramatically between pairs of situations in which what actually happened was identical, but where what would have happened differed. Experiment 2 features multiple 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, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain.

Causal Judgment in the Wild: Evidence from the 2020 U.S. Presidential Election

“…Gerstenberg and colleagues (2021) have reported similar results in the domain of intuitive physics. They were able to model the causal judgments that humans make about simple physical events by assuming that participants use a predictive physics engine to simulate counterfactuals.…”

“…We found that many existing formal models of causal judgment could not make clear quantitative predictions about the current election case (see Supplementary Information https://osf.io/6jgez/). For example, some are only designed to account for intuitions about physical events (e.g., Wolff, 2007; Gerstenberg et al., 2021), whereas others are only designed for scenarios with a simpler causal structure (e.g., Morris et al., 2018). We were able to derive clear predictions for two other models of causal judgment.…”

Modeling and leveraging intuitive theories to improve vaccine attitudes

“…Much of this type of thinking can be understood as deriving from people's intuitive theories-explanatory frameworks, analogous to scientific theories, that capture the causal and logical structure of the world within a domain. From early in life, people are driven to seek out such causal explanations and to use their mental representations of these causal systems to explain, predict, and intervene in the world (see Carey, 2009;Gelman & Legare, 2011;Gerstenberg et al, 2021;Gopnik & Wellman, 1994;Keil, 1994;Murphy & Medin, 1985; H. M. Wellman & Gelman, 1992 for influential treatments of this topic).…”

Double Prevention, Causal Judgments, and Counterfactuals

“…One of the most prominent ways to explain causal judgments is by employing counterfactual theories, on which causal judgments depend on counterfactual thinking (Gerstenberg et al, 2017(Gerstenberg et al, , 2021Hitchcock, 2007;Lewis, 1974;Paul, 2010). On such views, when someone wants to know if an event caused an outcome, they imagine a counterfactual alternative where the event did not happen and ask if the outcome still would have happened in that alternative scenario.…”

“…While counterfactual theories explain a wide array of causal judgments (Gerstenberg et al, 2021), cases of double prevention are a major challenge for our best theories of causal judgment (Bernstein, 2017;Hall, 2000Hall, , 2004Hitchcock, 2010;Paul, 2010;Woodward, 2012); counterfactual theories fail to explain why people tend accept the productive factor and reject the double preventer as the cause of the outcome in double-prevention scenarios. When someone imagines the counterfactual where Mike had not knocked into the bottle, the beer would not have spilled in this alternative scenario-ceteris paribus.…”