… to the propensity score, we set the calipers at .2 of the standard deviation of the logit of the
score … after propensity score matching. If the standardized difference was less than .1, the …

… Next, we discuss artificial intelligence (AI), an academic discipline started in 1956 (Crevier, …
AI is gaining popularity in the context of propensity score estimation. Artificial Intelligence. …

… Methods: We used the propensity score matching method to match two different groups of
respondents with similar demographic characteristics. Respondents were recruited in 2017 …

… propensity scores for binary and ternary treatments. This article aims to enable practitioners
to estimate propensity scores … as an alternative method for propensity score estimation, not a …

… We performed the first known AI and propensity score (PS)-augmented clinical, cost-… to
2018, using deep learning and machine learning augmented propensity score-adjusted (ML-PS) …

… We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic
information system (GIS) analysis of a multiyear and multisite cohort for OHCA outcomes (…

… propensity scores for any of these propensity score techniques. Propensity scores are …
In this paper, we examine the use of machine learning methods as one alternative to logistic …

… Buzzwords such as “Big Data”, “Machine Learning”, and “Artificial Intelligence” are currently
… on the implementation of an high-dimensional propensity score (hdPS) study of non-…

… This is the first known nationally representative artificial intelligence (AI) and propensity
score-augmented multi-year analysis of inpatient procedure utilization, mortality, cost, and …

… The objective of this paper was to apply the propensity score methodology to control for … In
conclusion, propensity score is an extensively used methodology in observational studies for …