Working Paper: NBER ID: w19453
Authors: James J. Heckman; Rodrigo Pinto
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 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.
Keywords: Causality; Haavelmo; Causal Models; Directed Acyclic Graphs; Econometrics
JEL Codes: C10; C18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| causal parameters (C29) | defined through hypothetical models (C20) |
| fixing (Y60) | independent variation of inputs (C29) |
| operation of fixing (L23) | causal effect of an input on an output (C67) |
| Haavelmo's approach (C59) | accommodates simultaneous causality (C32) |
| operation of fixing (L23) | equivalent to statistical conditioning (C29) |
| hypothetical model (C59) | flexible framework for analyzing causal relationships (C32) |
| identification of causal effects (C22) | achieved using standard statistical tools (C29) |