title: Propensity score estimation with boosted regression for evaluating causal effects in observational studies.
abstract: Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate estimation of propensity scores is impeded by large numbers of covariates, uncertain functional forms for their associations with treatment selection, and other problems. This article demonstrates that boosting, a modern statistical technique, can overcome many of these obstacles. The authors illustrate this approach with a study of adolescent probationers in substance abuse treatment programs. Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment.
authors.affiliation: Public Safety and Justice Program, Drug Policy Research Center, RAND Corporation, 201 North Craig Street, Pittsburgh, PA 15213, USA. danielm@rand.org
authors.forename: Daniel F
authors.initials: DF
authors.lastname: McCaffrey
authors.forename: Greg
authors.initials: G
authors.lastname: Ridgeway
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Morral
mesh_terms: D005069:Evaluation Studies as Topic; D006801:Humans; D008960:Models, Psychological; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D019966:Substance-Related Disorders / Q000628:therapy