Some PSM tutorials provide step-by-step guidance, but only one or two packages have been covered, thereby limiting their scope and practicality. While PSM tutorials are available in the literature, there is still room for improvement. Within this approach, propensity score matching (PSM) has been empirically proven, with outstanding performances across observational datasets. Propensity score methods, which are a series of balancing methods in these studies, have become increasingly popular by virtue of the two major advantages of dimension reduction and design separation. However, observational studies also have their drawbacks, mainly including the systematic differences in baseline covariates, which relate to outcomes between treatment and control groups that can potentially bias results. Observational studies have also attracted a great deal of attention as, quite often, large historical datasets are available for these kinds of studies. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical and cost issues. It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments.
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