Causal Analysis after Haavelmo
AbstractHaavelmo's seminal 1943 paper is the first rigorous treatment of causality. In it, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAG) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare an approach based on Haavelmo's methodology with a standard approach in the causal literature of DAGs– the "do-calculus" of Pearl (2009). We discuss the limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo (1944). In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover it.
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Blog mentionsAs found by EconAcademics.org, the blog aggregator for Economics research:
- Predictive Modeling, Causal Inference, and Imbens-Rubin (Among Others)
by Francis Diebold in No Hesitations on 2014-05-06 13:10:00
- Ran Spiegler, 2014. "Bayesian Networks and Boundedly Rational Expectations," Discussion Papers 1417, Centre for Macroeconomics (CFM).
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