Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Reverse causality is a common attribution error that distorts the evaluation of private actions and public policies. This paper explores the implications of this error when a decision maker acts on it and therefore affects the very statistical regularities from which he draws faulty inferences. Applying the Bayesian-network approach of Spiegler (2016), I explore the equilibrium effects of a certain class of reverse-causality errors, in the context of an example with a quadratic-normal parameterization. I show that the decision context may protect the decision maker from his own reverse-causality error. That is, the cost of reverse-causality errors can be lower for everyday decision makers than for an outside observer who evaluates their choices.