Literature DB >> 25729123

Causal Analysis After Haavelmo.

James Heckman1, Rodrigo Pinto2.   

Abstract

Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, 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 (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs-the "do-calculus" of Pearl (2009). We discuss the severe 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. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them.

Entities:  

Keywords:  Causality; Directed Acyclic Graphs; Do-Calculus; Identification; Simultaneous Treatment Effects

Year:  2015        PMID: 25729123      PMCID: PMC4341827          DOI: 10.1017/S026646661400022X

Source DB:  PubMed          Journal:  Econ Theory        ISSN: 0266-4666            Impact factor:   2.099


  2 in total

1.  Local instrumental variables and latent variable models for identifying and bounding treatment effects.

Authors:  J J Heckman; E J Vytlacil
Journal:  Proc Natl Acad Sci U S A       Date:  1999-04-13       Impact factor: 11.205

2.  Outline of a new principle of mathematical psychology (1851). By Gustav Theodor Fechner (translation)

Authors:  G T Fechner
Journal:  Psychol Res       Date:  1987
  2 in total
  7 in total

1.  Gender Differences in the Benefits of an Influential Early Childhood Program.

Authors:  Jorge Luis García; James J Heckman; Anna L Ziff
Journal:  Eur Econ Rev       Date:  2018-06-30

2.  Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models.

Authors:  Christian Gische; Manuel C Voelkle
Journal:  Psychometrika       Date:  2021-12-11       Impact factor: 2.290

3.  Returns to Education: The Causal Effects of Education on Earnings, Health, and Smoking.

Authors:  James J Heckman; John Eric Humphries; Gregory Veramendi
Journal:  J Polit Econ       Date:  2018-10

4.  Dynamic Treatment Effects.

Authors:  James J Heckman; John Eric Humphries; Gregory Veramendi
Journal:  J Econom       Date:  2016-02-01       Impact factor: 2.388

5.  Unordered Monotonicity.

Authors:  James J Heckman; Rodrigo Pinto
Journal:  Econometrica       Date:  2018-01       Impact factor: 5.844

6.  The Nonmarket Benefits of Education and Ability.

Authors:  James J Heckman; John Eric Humphries; Gregory Veramendi
Journal:  J Hum Cap       Date:  2018

7.  Decision-based models of the implementation of interventions in systems of healthcare: Implementation outcomes and intervention effectiveness in complex service environments.

Authors:  Arno Parolini; Wei Wu Tan; Aron Shlonsky
Journal:  PLoS One       Date:  2019-10-17       Impact factor: 3.240

  7 in total

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