Chasing Balance and Other Recommendations for Improving Nonparametric Propensity Score Models

Griffin Beth Ann (), McCaffrey Daniel F., Almirall Daniel, Burgette Lane F. and Setodji Claude Messan
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Griffin Beth Ann: RAND Corporation, Arlington, VA, USA
McCaffrey Daniel F.: ETS Research, Princeton, NJ, USA
Almirall Daniel: University of Michigan, Ann Arbor, MI, USA
Burgette Lane F.: RAND Corporation, Arlington, VA, USA
Setodji Claude Messan: RAND Corporation, Arlington, VA, USA

Journal of Causal Inference, 2017, vol. 5, issue 2, 18

Abstract: In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance and inferences about causal effects: pursuing covariate balance between the treatment groups or tuning the propensity score model on the basis of a model fit criterion. Second, we examine how well GBM can handle irrelevant covariates that are included in the estimation model. We find that chasing balance rather than model fit when estimating propensity scores yielded better covariate balance and more accurate treatment effect estimates. Additionally, we find that adding irrelevant covariates to GBM increased imbalance and bias in the treatment effects. The findings from this paper have useful implications for other work focused on improving methods for estimating propensity scores.

Keywords: propensity score; generalized boosted models; covariate balance; machine learning methods (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:5:y:2017:i:2:p:18:n:2

DOI: 10.1515/jci-2015-0026

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