03142cam a22002897 4500001000700000003000500007005001700012008004100029100001900070245017400089260006600263490004200329500001500371520178700386530006102173538007202234538003602306690006202342690015302404690005702557700002002614700002402634710004202658830007702700856003802777856003702815w14086NBER20170223181622.0170223s2008 mau||||fs|||| 000 0 eng d1 aBasu, Anirban.10aUse of Propensity Scores in Non-Linear Response Modelsh[electronic resource]:bThe Case for Health Care Expenditures /cAnirban Basu, Daniel Polsky, Willard G. Manning. aCambridge, Mass.bNational Bureau of Economic Researchc2008.1 aNBER working paper seriesvno. w14086 aJune 2008.3 aUnder 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. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web. 7aC01 - Econometrics2Journal of Economic Literature class. 7aC21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions2Journal of Economic Literature class. 7aI10 - General2Journal of Economic Literature class.1 aPolsky, Daniel.1 aManning, Willard G.2 aNational Bureau of Economic Research. 0aWorking Paper Series (National Bureau of Economic Research)vno. w14086.4 uhttp://www.nber.org/papers/w1408641uhttp://dx.doi.org/10.3386/w14086