R package causalweight: Causal Inference Based on Inverse Probability Weighting, Doubly Robust Estimation, and Double Machine Learning. Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020) <doi:10.1002/jae.2765>, and others.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Liangyuan Hu, Jiayi Ji: CIMTx: An R package for causal inference with multiple treatments using observational data (2021) arXiv
- Tianhui Zhou, Guangyu Tong, Fan Li, Laine E. Thomas, Fan Li: PSweight: An R Package for Propensity Score Weighting Analysis (2020) arXiv