Citation

Xie Y, Zhu Y, Cotton CA, Wu P. Stat. Methods Med. Res. 2019; 28(1): 84-101.

Affiliation

2 Value Institute, Christiana Care Health System, Newark, USA.

Copyright

(Copyright © 2019, SAGE Publishing)

DOI

10.1177/0962280217715487

PMID

28712346

Abstract

Many approaches, including traditional parametric modeling and machine learning techniques, have been proposed to estimate propensity scores. This paper describes a new model averaging approach to propensity score estimation in which parametric and nonparametric estimates are combined to achieve covariate balance. Simulation studies are conducted across different scenarios varying in the degree of interactions and nonlinearities in the treatment model. The results show that, based on inverse probability weighting, the proposed propensity score estimator produces less bias and smaller standard errors than existing approaches. They also show that a model averaging approach with the objective of minimizing the average Kolmogorov-Smirnov statistic leads to the best performing IPW estimator. The proposed approach is also applied to a real data set in evaluating the causal effect of formula or mixed feeding versus exclusive breastfeeding on a child's body mass index Z-score at age 4. The data analysis shows that formula or mixed feeding is more likely to lead to obesity at age 4, compared to exclusive breastfeeding.

Language: en

Keywords

Average causal effect; causal inference; covariate balance; model averaging; propensity scores