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Weighting Regressions by Propensity Scores

Author

Listed:
  • David A. Freedman

    (University of California, Berkeley, freedman@stat.berkeley.edu)

  • Richard A. Berk

    (University of Pennsylvania, berkr@sas.upenn.edu)

Abstract

Regressions can be weighted by propensity scores in order to reduce bias. However, weighting is likely to increase random error in the estimates, and to bias the estimated standard errors downward, even when selection mechanisms are well understood. Moreover, in some cases, weighting will increase the bias in estimated causal parameters. If investigators have a good causal model, it seems better just to fit the model without weights. If the causal model is improperly specified, there can be significant problems in retrieving the situation by weighting, although weighting may help under some circumstances.

Suggested Citation

  • David A. Freedman & Richard A. Berk, 2008. "Weighting Regressions by Propensity Scores," Evaluation Review, , vol. 32(4), pages 392-409, August.
    • Handle: RePEc:sae:evarev:v:32:y:2008:i:4:p:392-409
    DOI: 10.1177/0193841X08317586
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    References listed on IDEAS

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    Cited by:

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    2. Patrick Richard, 2016. "The Burden of Medical Debt Faced by Households with Dependent Children in the United States: Implications for the Affordable Care Act of 2010," Journal of Family and Economic Issues, Springer, vol. 37(2), pages 212-225, June.
    3. de Brauw, Alan & Gilligan, Daniel O. & Hoddinott, John F. & Roy, Shalini, 2014. "The impact of Bolsa Família on schooling: Girls’ advantage increases and older children gain:," IFPRI discussion papers 1319, International Food Policy Research Institute (IFPRI).
    4. Meredith Fowlie & Stephen P. Holland & Erin T. Mansur, 2012. "What Do Emissions Markets Deliver and to Whom? Evidence from Southern California's NOx Trading Program," American Economic Review, American Economic Association, vol. 102(2), pages 965-993, April.
    5. Despard, Mathieu R. & Perantie, Dana & Taylor, Samuel & Grinstein-Weiss, Michal & Friedline, Terri & Raghavan, Ramesh, 2016. "Student debt and hardship: Evidence from a large sample of low- and moderate-income households," Children and Youth Services Review, Elsevier, vol. 70(C), pages 8-18.
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    7. Li Liang & Greene Tom, 2013. "A Weighting Analogue to Pair Matching in Propensity Score Analysis," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 215-234, July.
    8. Robst, John & VanGilder, Jennifer, 2016. "Salary and job satisfaction among economics and business graduates: The effect of match between degree field and job," International Review of Economics Education, Elsevier, vol. 21(C), pages 30-40.

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