About James J. Heckman
Professor Heckman is the Henry Schultz Distinguished Service Professor of Economics at the University of Chicago. He won the Nobel Prize in Economics in 2000.
Heckman's work has been devoted to the development of a scientific basis for economic policy evaluation, with special emphasis on models of individuals and disaggregated groups, and to the problems and possibilities created by heterogeneity, diversity, and unobserved counterfactual states.
Building on Haavelmo's work, among others, Heckman developed a body of new econometric tools that address these issues.
His research has given policymakers important new insights into areas such as education, job training, the importance of accounting for general equilibrium in the analysis of labor markets, anti-discrimination law, and civil rights.
Among Norwegian policymakers he is most known for his work on the effects of children education.
About the lecture "Causal Analysis After Haavelmo"
In a number of works Heckman has discussed how to deal properly with causality analysis in econometric works, a topic on which there currently is some controversy. Heckman has drawn attention to the clear distinction between causation and correlation in the fundamental econometric and Nobel laureated contributions of Trygve Haavelmo from 1943 and 1944, and elaborated upon their importance for the study of causal inference.
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 inuential 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.
The paper is co-authored by Rodrigo Pinto, Research fellow, University of Chicago