Estimation of propensity scores using generalized additive models

Propensity score matching is often used in observational studies to create treatment and control groups with similar distributions of observed covariates. Typically, propensity scores are estimated using logistic regressions that assume linearity between the logistic link and the predictors. We evaluate the use of generalized additive models (GAMs) for estimating propensity scores. We compare logistic regressions and GAMs in terms of balancing covariates using simulation studies with artificial and genuine data. We find that, when the distributions of covarites in the treatment and control groups overlap sufficiently, using GAMs can improve overall covariate balance, especially for higher-order moments of distributions. When the distributions in the two groups overlap sufficiently, GAM more clearly reveals, thhis fact than logistic regression does. We also demonstrate via simulation that matchning with GAMs can result in larger reductions in bias when estimating treatment effects than matching with logistic regression. Copyright (c) 2008 John Wiley & Sons, Ltd.