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Bias-Adjusted Treatment Effects Under Equal Selection

Author

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  • Deepankar Basu

    (Department of Economics, University of Massachusetts Amherst)

Abstract

In a recent contribution, Oster (2019) has proposed a method to generate bounds on treatment effects in the presence of unobservable con- founders. The method can only be implemented if a crucial problem of non-uniqueness is addressed. In this paper I demonstrate that one of the proposed methods to address non-uniqueness that relies on computing bias-adjusted treatment effects under the assumption of equal selection on observables and unobservables, is problematic on several counts. First, additional assumptions, which cannot be justified on theoretical grounds, are needed to ensure a unique solution; second, the method will not work when estimate of the treatment effect declines with the addition of controls; and third, the solution, and therefore conclusions about bias, can change dramatically if we deviate from equal selection even by a small magnitude.

Suggested Citation

  • Deepankar Basu, 2021. "Bias-Adjusted Treatment Effects Under Equal Selection," UMASS Amherst Economics Working Papers 2021-05, University of Massachusetts Amherst, Department of Economics.
    • Handle: RePEc:ums:papers:2021-05
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    File URL: https://scholarworks.umass.edu/econ_workingpaper/302/
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    Cited by:

    1. Basu, Deepankar, 2021. "Majoritarian politics and hate crimes against religious minorities: Evidence from India, 2009–2018," World Development, Elsevier, vol. 146(C).

    More about this item

    Keywords

    treatment effect; omitted variable bias;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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