Comparison of various machine learning algorithms for estimating generalized propensity score

DOI10.1080/00949655.2019.1571059OpenAlexW2912189763WikidataQ128590102 ScholiaQ128590102MaRDI QIDQ5107349

Chunhao Tu

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2019.1571059

zbMATH Keywords

observational studiesbaggingrandom forestaverage treatment effectgradient boosting

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to social sciences (62P25)

Related Items (3)

Evaluating inverse propensity score weighting in the presence of many treatments. An application to the estimation of the neighbourhood effect ⋮ Estimating propensity scores using neural networks and traditional methods: a comparative simulation study ⋮ Using predictability to improve matching of urban locations in Philadelphia

Uses Software

• Unnamed Item
• MASS (R)
• nnet
• gbm
• ElemStatLearn

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Statistical Analysis of Financial Data in S-Plus
• The central role of the propensity score in observational studies for causal effects
• The role of the propensity score in estimating dose-response functions
• Using generalized doubly robust estimator to estimate average treatment effects of multiple treatments in observational studies
• Random forests
• Stochastic gradient boosting.

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