On the Behavioral Consequences of Reverse Causality

TL;DR

This paper examines how reverse causality errors influence decision-making and statistical regularities, revealing that decision contexts can sometimes mitigate the impact of such errors across various fields.

Contribution

It introduces a Bayesian-network approach to analyze the equilibrium effects of reverse causality errors, highlighting their subtle impacts in multiple domains.

Findings

Reverse causality errors can alter statistical regularities.

Decision contexts may reduce the impact of these errors.

Implications vary across psychology, economics, and policy.

Abstract

Reverse causality is a common causal misperception 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. Using a quadratic-normal parameterization and applying the Bayesian-network approach of Spiegler (2016), I demonstrate the subtle equilibrium effects of a certain class of reverse-causality errors, with illustrations in diverse areas: development psychology, social policy, monetary economics and IO. In particular, the decision context may protect the decision maker from his own reverse-causality causal 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.