Can propensity score matching replace randomized controlled trials?

Table 2 Comparison of methods of computing propensity scores

Method	Advantages	Disadvantages	Ref.
Logistic regression	Simple and widely used	May not capture complex or nonlinear relationships	Otok et al[6], 2017
	Can handle binary and continuous covariates	May be sensitive to model misspecification
	Can estimate the propensity score and the treatment effect in one model	May not balance all covariates well
Discriminant analysis	Can handle multiclass treatment	May not capture nonlinear relationships	Rudner and Johnette[7], 2006
	Can capture linear combinations of covariates	May be sensitive to outliers and distributional assumptions
	Can handle multicollinearity among covariates	May not balance all covariates well
Random forests	Can handle complex and nonlinear relationships	May be computationally intensive	Zhao et al[8], 2016
	Can handle binary, categorical, and continuous covariates	May overfit the data
	Can balance all covariates well	May not estimate the propensity score and the treatment effect in one model