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Using instrumental variables to establish causality

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  • Sascha O. Becker

    (University of Warwick, UK, and IZA, Germany)

Abstract

Randomized control trials are often considered the gold standard to establish causality. However, in many policy-relevant situations, these trials are not possible. Instrumental variables affect the outcome only via a specific treatment; as such, they allow for the estimation of a causal effect. However, finding valid instruments is difficult. Moreover, instrumental variables estimates recover a causal effect only for a specific part of the population. While those limitations are important, the objective of establishing causality remains; and instrumental variables are an important econometric tool to achieve this objective.

Suggested Citation

  • Sascha O. Becker, 2016. "Using instrumental variables to establish causality," IZA World of Labor, Institute of Labor Economics (IZA), pages 250-250, April.
    • Handle: RePEc:iza:izawol:journl:y:2016:n:250
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    References listed on IDEAS

    as
    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. Ichino, Andrea & Winter-Ebmer, Rudolf, 1999. "Lower and upper bounds of returns to schooling: An exercise in IV estimation with different instruments," European Economic Review, Elsevier, vol. 43(4-6), pages 889-901, April.
    3. Paul J. Devereux & Robert A. Hart, 2010. "Forced to be Rich? Returns to Compulsory Schooling in Britain," Economic Journal, Royal Economic Society, vol. 120(549), pages 1345-1364, December.
    4. Andrews,Donald W. K. & Stock,James H. (ed.), 2005. "Identification and Inference for Econometric Models," Cambridge Books, Cambridge University Press, number 9780521844413, January.
    5. Timothy G. Conley & Christian B. Hansen & Peter E. Rossi, 2012. "Plausibly Exogenous," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 260-272, February.
    6. repec:fth:prinin:455 is not listed on IDEAS
    7. Joshua D. Angrist & Alan B. Keueger, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 979-1014.
    8. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    natural experiments; quasi-natural experiments; treatment effects; local average treatment effect; omitted variable bias; reverse causality;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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