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Bounding a linear causal effect using relative correlation restrictions

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Abstract

This paper describes and implements a simple approach to the most common problem in applied microeconometrics: estimating a linear causal effct when the explanatory variable of interest might be correlated with relevant unobserved variables. The main idea is to place restrictions on the correlation between the variable of interest and relevant unobserved variables relative to the correlation between the variable of interest and observed control variables. These relative correlation restrictions allow a researcher to construct informative bounds on parameter estimates, and to assess the sensitivity of conventional estimates to plausible deviations from the identifying assumptions. The estimation method and its properties are described, and two empirical applications are demonstrated.

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  • Brian Krauth, 2011. "Bounding a linear causal effect using relative correlation restrictions," Discussion Papers dp11-02, Department of Economics, Simon Fraser University.
    • Handle: RePEc:sfu:sfudps:dp11-02
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    8. Halkos, George E. & Paizanos, Epameinondas Α., 2013. "The effect of government expenditure on the environment:An empirical investigation," Ecological Economics, Elsevier, vol. 91(C), pages 48-56.
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    10. Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso & Parisian, Daniel J., 2016. "The effect of degree attainment on arrests: Evidence from a randomized social experiment," Economics of Education Review, Elsevier, vol. 54(C), pages 259-273.
    11. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2024. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 1-13, January.
    12. Bensch, Gunther & Kluve, Jochen & Stöterau, Jonathan, 2021. "The market-based dissemination of energy-access technologies as a business model for rural entrepreneurs: Evidence from Kenya," Resource and Energy Economics, Elsevier, vol. 66(C).
    13. KITAGAWA, Toru & SAWADA, Masayuki, 2023. "Linear Regressions, Shorts to Long," Discussion Paper Series 747, Institute of Economic Research, Hitotsubashi University.
    14. Bennett, Fidel & Contreras, Dante & Morales Cerda, Matías, 2022. "The consequences of exclusionary discipline on school dropout: Evidence from Chile," International Journal of Educational Development, Elsevier, vol. 95(C).
    15. Halkos, George & Paizanos, Epameinondas, 2014. "Exploring the effect of economic growth and government expenditure on the environment," MPRA Paper 56084, University Library of Munich, Germany.
    16. Matthew A. Masten & Alexandre Poirier, 2022. "The Effect of Omitted Variables on the Sign of Regression Coefficients," Papers 2208.00552, arXiv.org, revised Feb 2023.
    17. Lall, Saurabh A. & Chen, Li-Wei & Roberts, Peter W., 2020. "Are we accelerating equity investment into impact-oriented ventures?," World Development, Elsevier, vol. 131(C).
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    20. Ransom, Michael R & Ransom, Tyler, 2018. "Do high school sports build or reveal character? Bounding causal estimates of sports participation," Economics of Education Review, Elsevier, vol. 64(C), pages 75-89.

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    More about this item

    Keywords

    sensitivity analysis; partial identification; endogeneity;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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