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Viewpoint: The human capital approach to inference

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  • W. Bentley MacLeod

Abstract

The purpose of this essay is to discuss two approaches to inference and how “human capital” can provide a way to combine them. The first approach, ubiquitous in economics, is based upon the Rubin–Holland potential outcomes model and relies upon randomized treatment to measure the causal effect of choice. The second approach, widely used in the pattern recognition and machine learning literatures, assumes that choice conditional upon current information is optimal (or at least high quality), and then provides techniques to generalize observed choice to new cases. The “human capital” approach combines these methods by using observed decisions by experts to reduce the dimensionality of the feature space and allow the categorization of decisions by their propensity score. The fact that the human capital of experts is heterogeneous implies that errors in decision making are inevitable. Moreover, under the appropriate conditions, these decisions are random conditional upon the propensity score. This in turn allows us to identify the conditional average treatment effect for a wider class of situations than would be possible with randomized control trials. This point is illustrated with data from medical decision making in the context of treating depression, heart disease and adverse childbirth events. Point de vue : L'approche à l'inférence par le capital humain. Le but de cet essai est de discuter deux approches à l’inférence, et de montrer comment le ≪ capital humain ≫ suggère une façon de les combiner. La première approche, omniprésente en science économique, est fondée sur le modèle Rubin/Holland des résultats potentiels, et repose sur un traitement randomisé pour mesurer l’effet causal des choix. La seconde approche, vastement utilisée dans la reconnaissance des patterns et dans la littérature sur l’apprentissage machine, présume que le choix conditionné par l’information disponible est optimal (ou au moins de haute qualité), et fournit des techniques pour généraliser les choix observés à de nouveaux cas de figure. L’approche par le ≪ capital humain ≫ combine ces méthodes en utilisant les décisions observées des experts pour réduire la dimensionnalité de l’espace de décision et permettre la catégorisation des décisions selon leur propensity scope. Le fait que le capital humain des experts est hétérogène implique que des erreurs dans la prise de décision sont inévitables. De plus, sous certaines conditions appropriées, ces décisions sont aléatoirement conditionnées par le propensity score. Voilà qui permet d’identifier l’effet du traitement conditionnel moyen pour une classe plus vaste de situations que ce qui est possible avec des essais contrôlés et randomisés. Ce point est illustré avec des données de prises de décision médicales dans un contexte de traitement de la dépression, de maladie cardiaque, et d’incidents indésirables lors de l’accouchement.

Suggested Citation

  • W. Bentley MacLeod, 2017. "Viewpoint: The human capital approach to inference," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(1), pages 5-39, February.
  • Handle: RePEc:wly:canjec:v:50:y:2017:i:1:p:5-39
    DOI: 10.1111/caje.12249
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    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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