  We show that it is possible to understand and identify a decision maker's
subjective causal judgements by observing her preferences over interventions.
Following Pearl [2000], we represent causality using causal models (also called
structural equations models), where the world is described by a collection of
variables, related by equations. We show that if a preference relation over
interventions satisfies certain axioms (related to standard axioms regarding
counterfactuals), then we can define (i) a causal model, (ii) a probability
capturing the decision-maker's uncertainty regarding the external factors in
the world and (iii) a utility on outcomes such that each intervention is
associated with an expected utility and such that intervention $A$ is preferred
to $B$ iff the expected utility of $A$ is greater than that of $B$. In
addition, we characterize when the causal model is unique. Thus, our results
allow a modeler to test the hypothesis that a decision maker's preferences are
consistent with some causal model and to identify causal judgements from
observed behavior.
