Use of propensity scores in non-linear response models: The case for health care expenditures
AbstractUnder the assumption of no unmeasured confounders, a large literature exists on methods that can be used to estimating average treatment effects (ATE) from observational data and that spans regression models, propensity score adjustments using stratification, weighting or regression and even the combination of both as in doubly-robust estimators. However, comparison of these alternative methods is sparse in the context of data generated via nonlinear models where treatment effects are heterogeneous, such as is in the case of healthcare cost data. In this paper, we compare the performance of alternative regression and propensity score-based estimators in estimating average treatment effects on outcomes that are generated via non-linear models. Using simulations, we find that in moderate size samples (n= 5000), balancing on estimated propensity scores balances the covariate means across treatment arms but fails to balance higher-order moments and covariances amongst covariates, raising concern about its use in non-linear outcomes generating mechanisms. We also find that besides inverse-probability weighting (IPW) with propensity scores, no one estimator is consistent under all data generating mechanisms. The IPW estimator is itself prone to inconsistency due to misspecification of the model for estimating propensity scores. Even when it is consistent, the IPW estimator is usually extremely inefficient. Thus care should be taken before naively applying any one estimator to estimate ATE in these data. We develop a recommendation for an algorithm which may help applied researchers to arrive at the optimal estimator. We illustrate the application of this algorithm and also the performance of alternative methods in a cost dataset on breast cancer treatment.
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Bibliographic InfoPaper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 08/11.
Date of creation: May 2008
Date of revision:
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More information through EDIRC
Propensity score; Non-linear regression; average treatment effect; Healthcare costs;
Other versions of this item:
- Anirban Basu & Daniel Polsky & Willard G. Manning, 2008. "Use of Propensity Scores in Non-Linear Response Models: The Case for Health Care Expenditures," NBER Working Papers 14086, National Bureau of Economic Research, Inc.
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- I10 - Health, Education, and Welfare - - Health - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-06-07 (All new papers)
- NEP-ECM-2008-06-07 (Econometrics)
- NEP-HEA-2008-06-07 (Health Economics)
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