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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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    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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