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A note on Covariate Balancing Propensity Score and Instrument-like variables

Author

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  • Adeola Oyenubi

    (School of Economics and Fiance, University of the Witwatersrand)

Abstract

We use the term instrument-like variables to describe a variable that is highly correlated with treatment (or programme participation) and weakly correlated with outcome. This kind of variable cannot be used in instrumental variable estimation because they are not instruments and the literature also show that they should not also be used in Propensity Score Matching (PSM) because of their high correlation with treatment. The literature is therefore silent on the estimation approach that performs better when it is necessary to control for such an instrument-like variable. In this paper, we consider the estimation of treatment effect in the presence of an instrument-like variable and an unobserved confounder. The result shows that a particular variant of propensity score estimation namely Covariate Balancing Propensity Score (CBPS) performs better than alternatives in the presence of instrument-like variable and an unmeasured confounder. Our simulation result suggests that by trading off treatment prediction for balance CBPS reduces the influence of instrument-like variables on the propensity scores. This leads to lower bias and mean square error for the estimate that is based on the CBPS model.

Suggested Citation

  • Adeola Oyenubi, 2020. "A note on Covariate Balancing Propensity Score and Instrument-like variables," Economics Bulletin, AccessEcon, vol. 40(1), pages 202-209.
    • Handle: RePEc:ebl:ecbull:eb-19-00758
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    References listed on IDEAS

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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
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    12. Adeola Oyenubi, 2020. "Optimising balance using covariate balancing propensity score: The case of South African child support grant," Development Southern Africa, Taylor & Francis Journals, vol. 37(4), pages 570-586, July.
    13. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
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    Cited by:

    1. Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).

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    More about this item

    Keywords

    Causal inference; Instrumental variables; Observational studies; Propensity score matching;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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