IDEAS home Printed from https://ideas.repec.org/a/sae/evarev/v32y2008i4p392-409.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0193841X08317586
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0193841X08317586?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. repec:mpr:mprres:3694 is not listed on IDEAS
    3. Steven Glazerman & Dan M. Levy & David Myers, 2003. "Nonexperimental Versus Experimental Estimates of Earnings Impacts," The ANNALS of the American Academy of Political and Social Science, , vol. 589(1), pages 63-93, September.
    4. Cheti Nicoletti & Marco Francesconi, 2006. "Intergenerational mobility and sample selection in short panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1265-1293.
    5. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    6. Elizabeth Ty Wilde & Robinson Hollister, 2007. "How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 26(3), pages 455-477.
    7. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    8. Arceneaux, Kevin & Gerber, Alan S. & Green, Donald P., 2006. "Comparing Experimental and Matching Methods Using a Large-Scale Voter Mobilization Experiment," Political Analysis, Cambridge University Press, vol. 14(1), pages 37-62, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. de Brauw, Alan & Gilligan, Daniel O. & Hoddinott, John & Roy, Shalini, 2014. "The Impact of Bolsa Família on Women’s Decision-Making Power," World Development, Elsevier, vol. 59(C), pages 487-504.
    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. 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.
    6. 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.
    7. 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.
    8. Eriksen, Steffen & Wiese, Rasmus, 2019. "Policy induced increases in private healthcare financing provide short-term relief of total healthcare expenditure growth: Evidence from OECD countries," European Journal of Political Economy, Elsevier, vol. 59(C), pages 71-82.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    2. David A. Freedman, 2009. "Limits of Econometrics," International Econometric Review (IER), Econometric Research Association, vol. 1(1), pages 5-17, April.
    3. Hisaki Kono & Yasuyuki Sawada & Abu S. Shonchoy, 2016. "DVD-based Distance-learning Program for University Entrance Exams: Experimental Evidence from Rural Bangladesh," CIRJE F-Series CIRJE-F-1027, CIRJE, Faculty of Economics, University of Tokyo.
    4. 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.
    5. Katherine Baicker & Theodore Svoronos, 2019. "Testing the Validity of the Single Interrupted Time Series Design," NBER Working Papers 26080, National Bureau of Economic Research, Inc.
    6. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.
    7. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    8. Kenneth Fortson & Natalya Verbitsky-Savitz & Emma Kopa & Philip Gleason, 2012. "Using an Experimental Evaluation of Charter Schools to Test Whether Nonexperimental Comparison Group Methods Can Replicate Experimental Impact Estimates," Mathematica Policy Research Reports 27f871b5b7b94f3a80278a593, Mathematica Policy Research.
    9. Donna Feir & Kelly Foley & Maggie E. C. Jones, 2021. "The Distributional Impacts of Active Labor Market Programs for Indigenous Populations," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 216-220, May.
    10. Ben Weidmann & Luke Miratrix, 2021. "Lurking Inferential Monsters? Quantifying Selection Bias In Evaluations Of School Programs," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(3), pages 964-986, June.
    11. Hällsten, Martin, 2012. "Is it ever too late to study? The economic returns on late tertiary degrees in Sweden," Economics of Education Review, Elsevier, vol. 31(1), pages 179-194.
    12. Kenneth Fortson & Philip Gleason & Emma Kopa & Natalya Verbitsky-Savitz, "undated". "Horseshoes, Hand Grenades, and Treatment Effects? Reassessing Bias in Nonexperimental Estimators," Mathematica Policy Research Reports 1c24988cd5454dd3be51fbc2c, Mathematica Policy Research.
    13. David J. Harding & Lisa Sanbonmatsu & Greg J. Duncan & Lisa A. Gennetian & Lawrence F. Katz & Ronald C. Kessler & Jeffrey R. Kling & Matthew Sciandra & Jens Ludwig, 2023. "Evaluating Contradictory Experimental and Nonexperimental Estimates of Neighborhood Effects on Economic Outcomes for Adults," Housing Policy Debate, Taylor & Francis Journals, vol. 33(2), pages 453-486, March.
    14. Jared Coopersmith & Thomas D. Cook & Jelena Zurovac & Duncan Chaplin & Lauren V. Forrow, 2022. "Internal And External Validity Of The Comparative Interrupted Time‐Series Design: A Meta‐Analysis," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(1), pages 252-277, January.
    15. Fatih Unlu & Douglas Lee Lauen & Sarah Crittenden Fuller & Tiffany Berglund & Elc Estrera, 2021. "Can Quasi‐Experimental Evaluations That Rely On State Longitudinal Data Systems Replicate Experimental Results?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(2), pages 572-613, March.
    16. David M. Rindskopf & William R. Shadish & M. H. Clark, 2018. "Using Bayesian Correspondence Criteria to Compare Results From a Randomized Experiment and a Quasi-Experiment Allowing Self-Selection," Evaluation Review, , vol. 42(2), pages 248-280, April.
    17. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    18. Fortson, Kenneth & Gleason, Philip & Kopa, Emma & Verbitsky-Savitz, Natalya, 2015. "Horseshoes, hand grenades, and treatment effects? Reassessing whether nonexperimental estimators are biased," Economics of Education Review, Elsevier, vol. 44(C), pages 100-113.
    19. Dridi, Ichrak & Boughrara, Adel, 2023. "Flexible inflation targeting and stock market volatility: Evidence from emerging market economies," Economic Modelling, Elsevier, vol. 126(C).
    20. Ao Yuan & Anqi Yin & Ming T. Tan, 2021. "Enhanced Doubly Robust Procedure for Causal Inference," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 454-478, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:evarev:v:32:y:2008:i:4:p:392-409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.