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Graphical Statistical Methods for Studying Causal Effects. Bayesian Networks

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  • Slutskin, L.

    (Institute of Economics of the Russian Academy of Sciences (IE RAS), Moscow, Russia)

Abstract

Revival of interest to statistical causality theory from the beginning of the 1990s was brought about, in our opinion, by two reasons. First, realization of the fact that Markovian properties allow, though partial, statistical testing of causal relations a priori determined by a researcher. Second, introduction to statistics of operator "do" by Judea Pearl (Pearl, 1995). The latter contributed to a great extent to basing ausality theory upon formal probability theory and understanding causal effects as external interference in data generating process. The paper has three major objectives: 1) to present, on a sufficiently rigorous mathematical level, basic concepts and ideas of modern statistical causality theory based on graphical representation of Bayesian networks; 2) to demonstrate how graphical methods of statistical causality theory can be applied to economics and economic policy by means of "do" operator; 3) to show how those methods may be effectively used for determining indirect causes of economic factors. For this purpose we developed a new method of representing the Bayesian graph as a sequence of layers about the factor under consideration.

Suggested Citation

  • Slutskin, L., 2017. "Graphical Statistical Methods for Studying Causal Effects. Bayesian Networks," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 12-30.
  • Handle: RePEc:nea:journl:y:2017:i:36:p:12-30
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    References listed on IDEAS

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    1. J. Tinbergen, 1940. "Econometric Business Cycle Research," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 7(2), pages 73-90.
    2. Henry L. Bryant & David A. Bessler & Michael S. Haigh, 2006. "Causality in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(11), pages 1039-1057, November.
    3. Jin Zhang & David Bessler & David Leatham, 2006. "Does consumer debt cause economic recession? Evidence using directed acyclic graphs," Applied Economics Letters, Taylor & Francis Journals, vol. 13(7), pages 401-407.
    4. Aivazian, Sergei, 2008. "Bayesian Methods in Econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 9(1), pages 93-130.
    5. Slutskin, Lev, 2015. "Definition of a prior distribution in Bayesian analysis by minimizing Kullback–Leibler divergence under data availability," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 40(4), pages 129-141.
    6. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    7. Shemyakin, Arkady, 2012. "A new approach to construction of objective priors: Hellinger information," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 28(4), pages 124-137.
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    Cited by:

    1. Ekaterina V. Orlova, 2023. "Inference of Factors for Labor Productivity Growth Used Randomized Experiment and Statistical Causality," Mathematics, MDPI, vol. 11(4), pages 1-22, February.

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    More about this item

    Keywords

    Bayesian networks; path coefficients; Markov conditions; oriented graphs; causation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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