首页|Machine Learning for Propensity Score Estimation: A Systematic Review and Report ing Guidelines
Machine Learning for Propensity Score Estimation: A Systematic Review and Report ing Guidelines
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from os f.io: “Machine learning has become a common approach for estimating propensity scores for quasiexperimental research using matching, weighting, or stratification on the propensity score. “This systematic review examined machine learning applications for propensity sc ore estimation across different fields, such as health, education, social scienc es, and business over 40 years. The results show that the gradient boosting mach ine (GBM) is the most frequently used method, followed by random forest. Classif ication and regression trees (CART), neural networks, and the super learner were also used in more than five percent of studies. The most frequently used packag es to estimate propensity scores were twang, gbm and randomforest in the R stati stical software. The review identified many hyperparameter configurations used f or machine learning methods.
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