Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using embarrassingly simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, identification, mediation and introspection. Finally, we observe that modern economists are largely unaware of the benefits that Haavelmo’s ideas bestow upon them and, as a result, econometric research has not fully utilized modern advances in causal analysis. 1

direct and indirect effects

Recent advances in graphical models and the logic of causation have given rise to new ways in which scientists analyze cause-effect relationships. Today, we understand precisely the conditions under which causal relationships can be inferred from data, the assumptions and measurements needed for predicting the effect of interventions (e.g., treatments on recovery) and how retrospective counterfactuals (e.g., “I should have done it differently”) can be reasoned about algorithmically or derived from data. The paper provides a brief account of these developments.