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The prognostic analogue of the propensity score

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  • Ben B. Hansen

Abstract

The propensity score collapses the covariates of an observational study into a single measure summarizing their joint association with treatment conditions; prognostic scores summarize covariates' association with potential responses. As with propensity scores, stratification on prognostic scores brings to uncontrolled studies a concrete and desirable form of balance, a balance that is more familiar as an objective of experimental control. Like propensity scores, prognostic scores can reduce the dimension of the covariate, yet causal inferences conditional on them are as valid as are inferences conditional only on the unreduced covariate. As a method of adjustment unto itself, prognostic scoring has limitations not shared with propensity scoring, but it holds promise as a complement to the propensity score, particularly in certain designs for which unassisted propensity adjustment is difficult or infeasible. Copyright 2008, Oxford University Press.

Suggested Citation

  • Ben B. Hansen, 2008. "The prognostic analogue of the propensity score," Biometrika, Biometrika Trust, vol. 95(2), pages 481-488.
  • Handle: RePEc:oup:biomet:v:95:y:2008:i:2:p:481-488
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    File URL: http://hdl.handle.net/10.1093/biomet/asn004
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    Cited by:

    1. de Luna, Xavier & Johansson, Per & Sjöstedt-de Luna, Sara, 2010. "Bootstrap inference for K-nearest neighbour matching estimators," Working Paper Series 2010:13, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    2. Cory Koedel & Diyi Li & Morgan S. Polikoff & Tenice Hardaway & Stephani L. Wrabel, 2016. "Mathematics Curriculum Effects on Student Achievement in California," Working Papers 1612, Department of Economics, University of Missouri.
    3. repec:spr:stpapr:v:58:y:2017:i:4:d:10.1007_s00362-016-0745-z is not listed on IDEAS
    4. Jenny Häggström & Xavier Luna, 2014. "Targeted smoothing parameter selection for estimating average causal effects," Computational Statistics, Springer, vol. 29(6), pages 1727-1748, December.
    5. Dan Yang & Dylan S. Small & Jeffrey H. Silber & Paul R. Rosenbaum, 2012. "Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes," Biometrics, The International Biometric Society, vol. 68(2), pages 628-636, June.
    6. Thomas C. Buchmueller & John DiNardo & Robert G. Valletta, 2011. "The Effect of an Employer Health Insurance Mandate on Health Insurance Coverage and the Demand for Labor: Evidence from Hawaii," American Economic Journal: Economic Policy, American Economic Association, vol. 3(4), pages 25-51, November.
    7. repec:oup:biomet:v:104:y:2017:i:3:p:583-596. is not listed on IDEAS
    8. José R. Zubizarreta, 2012. "Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1360-1371, December.

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