Noisy Newtons: Unifying process and dependency accounts of causal attribution


There is a long tradition in both philosophy and psychology to separate process accounts from dependency accounts of causation. In this paper, we motivate a unifying account that explains people's causal attributions in terms of counterfactuals defined over probabilistic generative models. In our experiments, participants see two billiard balls colliding and indicate to what extent ball A caused/prevented ball B to go through a gate. Our model predicts that people arrive at their causal judgments by comparing what actually happened with what they think would have happened, had the collision between A and B not taken place. Participants' judgments about what would have happened are highly correlated with a noisy model of Newtonian physics. Using those counterfactual judgments, we can predict participants' cause and prevention judgments very accurately (r = .99). Our framework also allows us to capture intrinsically counterfactual judgments such as almost caused/prevented.

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