Noisy Newtons: Unifying process and dependency accounts of causal attribution
Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado, Joshua B. Tenenbaum · eScholarship (California Digital Library) · 2012
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.