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A Note on Endogenous Control Variables in Evaluation Studies


  • Michael Lechner



The paper addresses the issue of potentially endogenous control variables in evaluation studies based on the assumption that selection bias can be avoided by conditioning on observed variables. It shows that the standard formulation of the conditional independence (CIA) or selection on observables assumption obscures the endogeneity problem. The paper clarifies the issue by suggesting a CIA based on exogenous (potential) outcomes as well as explicit exogeneity conditions. The refined CIA allows to asses the endogeneity bias as well as the plausibility of the CIA, because it allows a separate discussion of (i) the problems of missing variables that are necessary to avoid selection bias, and (ii) the problem that some of the control variables may be influenced by the treatment.

Suggested Citation

  • Michael Lechner, 2005. "A Note on Endogenous Control Variables in Evaluation Studies," University of St. Gallen Department of Economics working paper series 2005 2005-16, Department of Economics, University of St. Gallen.
  • Handle: RePEc:usg:dp2005:2005-16

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    References listed on IDEAS

    1. Donald B. Rubin, 2004. "Direct and Indirect Causal Effects via Potential Outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 161-170.
    2. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
    3. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    4. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
    5. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    6. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
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    Cited by:

    1. Poppe, Robert, 2007. "The matching approach on expenditure patterns of migrant households: evidence from Moldova," Kiel Advanced Studies Working Papers 444, Kiel Institute for the World Economy (IfW).
    2. Gueorgui Kambourov & Iourii Manovskii & Miana Plesca, 2012. "Occupational Mobility and the Returns to Training," Working Papers tecipa-444, University of Toronto, Department of Economics.
    3. Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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