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 causa-tion. 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, par-ticipants 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 hap-pened 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.