Health Services and Outcomes Research Methodology

, Volume 11, Issue 1, pp 1-26

First online:

Estimating treatment effects on healthcare costs under exogeneity: is there a ‘magic bullet’?

  • Anirban BasuAffiliated withDepartment of Health Services and PORPP, University of WashingtonThe National Bureau of Economic Research Email author 
  • , Daniel PolskyAffiliated withDivision of General Internal Medicine, University of Pennsylvania
  • , Willard G. ManningAffiliated withHarris School of Public Policy Studies, University of Chicago

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Methods for estimating average treatment effects (ATEs), under the assumption of no unmeasured confounders, include regression models; propensity score (PS) adjustments using stratification, weighting, or matching; and doubly robust estimators (a combination of both). Researchers continue to debate about the best estimator for outcomes such as health care cost data, as they are usually characterized by an asymmetric distribution and heterogeneous treatment effects,. Challenges in finding the right specifications for regression models are well documented in the literature. Propensity score estimators are proposed as alternatives to overcoming these challenges. Using simulations, we find that in moderate size samples (n = 5,000), balancing on PSs that are estimated from saturated specifications can balance the covariate means across treatment arms but fails to balance higher-order moments and covariances amongst covariates. Therefore, unlike regression model, even if a formal model for outcomes is not required, PS estimators can be inefficient at best and biased at worst for health care cost data. Our simulation study, designed to take a ‘proof by contradiction’ approach, proves that no one estimator can be considered the best under all data generating processes for outcomes such as costs. The inverse-propensity weighted estimator is most likely to be unbiased under alternate data generating processes but is prone to bias under misspecification of the PS model and is inefficient compared to an unbiased regression estimator. Our results show that there are no ‘magic bullets’ when it comes to estimating treatment effects in health care costs. Care should be taken before naively applying any one estimator to estimate ATEs in these data. We illustrate the performance of alternative methods in a cost dataset on breast cancer treatment.


Propensity score Non-linear regression Average treatment effect Health care costs

JEL classification

C01 C21 I10