TY - JOUR
AU - Basu,Anirban
AU - Polsky,Daniel
AU - Manning,Willard G.
TI - Use of Propensity Scores in Non-Linear Response Models: The Case for Health Care Expenditures
JF - National Bureau of Economic Research Working Paper Series
VL - No. 14086
PY - 2008
Y2 - June 2008
DO - 10.3386/w14086
UR - http://www.nber.org/papers/w14086
L1 - http://www.nber.org/papers/w14086.pdf
N1 - Author contact info:
Anirban Basu
Departments of Health Services,
Pharmacy and Economics
University of Washington
1959 NE Pacific St
Box - 357660
Seattle WA 98195
Tel: 206) 616-2986
Fax: (206) 543-3964
E-Mail: basua@uw.edu
Daniel Polsky
University of Pennsylvania
Leonard Davis Institute of Health Economics
3641 Locust Walk
Philadelphia, PA 19104
E-Mail: polsky@mail.med.upenn.edu
Willard G. Manning
University of Chicago
Harris School of Public Policy Studies
1155 East 60th Street, Room 176
Chicago, IL 60637
E-Mail: w-manning@uchicago.edu
AB - Under 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 non-linear 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.
ER -