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Linear Models: A Useful “Microscope” for Causal Analysis

Author

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  • Pearl Judea

    (Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA)

Abstract

This note reviews basic techniques of linear path analysis and demonstrates, using simple examples, how causal phenomena of non-trivial character can be understood, exemplified and analyzed using diagrams and a few algebraic steps. The techniques allow for swift assessment of how various features of the model impact the phenomenon under investigation. This includes: Simpson’s paradox, case–control bias, selection bias, missing data, collider bias, reverse regression, bias amplification, near instruments, and measurement errors.

Suggested Citation

  • Pearl Judea, 2013. "Linear Models: A Useful “Microscope” for Causal Analysis," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 155-170, June.
    • Handle: RePEc:bpj:causin:v:1:y:2013:i:1:p:155-170:n:6
    DOI: 10.1515/jci-2013-0003
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    References listed on IDEAS

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    1. James Heckman, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    2. Jay Bhattacharya & William B. Vogt, 2007. "Do Instrumental Variables Belong in Propensity Scores?," NBER Technical Working Papers 0343, National Bureau of Economic Research, Inc.
    3. Arthur S. Goldberger, 1984. "Reverse Regression and Salary Discrimination," Journal of Human Resources, University of Wisconsin Press, vol. 19(3), pages 293-318.
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    Cited by:

    1. RUSSO, Federica & MOUCHART, Michel & WUNSCH, Guillaume, 2013. "Confounding and control in a multivariate system. An issue in causal attribution," LIDAM Discussion Papers CORE 2013068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Burkhard Raunig, 2019. "Background Indicators," Econometrics, MDPI, vol. 7(2), pages 1-14, May.

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