Comparison of various machine learning algorithms for estimating generalized propensity score

ABSTRACT

In this paper, we conducted a simulation study to evaluate the performance of four algorithms: multinomial logistic regression (MLR), bagging (BAG), random forest (RF), and gradient boosting (GB), for estimating generalized propensity score (GPS). Similar to the propensity score (PS), the ultimate goal of using GPS is to estimate unbiased average treatment effects (ATEs) in observational studies. We used the GPS estimates computed from these four algorithms with the generalized doubly robust (GDR) estimator to estimate ATEs in observational studies. We evaluated these ATE estimates in terms of bias and mean squared error (MSE). Simulation results show that overall, the GB algorithm produced the best ATE estimates based on these evaluation criteria. Thus, we recommend using the GB algorithm for estimating GPS in practice.

KEYWORDS:

• Average treatment effect
• bagging
• gradient boosting
• observational studies
• random forest

SUBJECT CLASSIFICATION CODES:

• 62P10
• 62P25

Disclosure statement

No potential conflict of interest was reported by the author.