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Causal Analysis after Haavelmo

James Heckman and Rodrigo Pinto

No 7628, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Haavelmo'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 de fined 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.

Keywords: simultaneous treatment effects; causality; identification; do-calculus; directed acyclic graphs (search for similar items in EconPapers)
JEL-codes: C10 C18 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2013-09
New Economics Papers: this item is included in nep-ecm and nep-hpe
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Published - revised version published in: Econometric Theory, 2015, 31(1), 115-151

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Related works:
Journal Article: CAUSAL ANALYSIS AFTER HAAVELMO (2015) Downloads
Working Paper: Causal Analysis after Haavelmo (2013) Downloads
Working Paper: Causal Analysis after Haavelmo (2013) Downloads
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