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Proxy variables and nonparametric identification of causal effects

Author

Listed:
  • de Luna, Xavier
  • Fowler, Philip
  • Johansson, Per

Abstract

Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcomes framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.

Suggested Citation

  • de Luna, Xavier & Fowler, Philip & Johansson, Per, 2017. "Proxy variables and nonparametric identification of causal effects," Economics Letters, Elsevier, vol. 150(C), pages 152-154.
    • Handle: RePEc:eee:ecolet:v:150:y:2017:i:c:p:152-154
    DOI: 10.1016/j.econlet.2016.11.018
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    References listed on IDEAS

    as
    1. Frost, Peter A, 1979. "Proxy Variables and Specification Bias," The Review of Economics and Statistics, MIT Press, vol. 61(2), pages 323-325, May.
    2. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    3. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Average treatment effect; Observational studies; Potential outcomes; Unobserved confounders;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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