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causalweight: Estimation Methods for Causal Inference Based on Inverse Probability Weighting
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.
Getting started
Browse package contents
Package details |
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| Author | Hugo Bodory [aut, cre] (<https://orcid.org/0000-0002-3645-1204>), Martin Huber [aut] (<https://orcid.org/0000-0002-8590-9402>), Jannis Kueck [aut] (<https://orcid.org/0000-0003-4367-0285>) |
| Maintainer | Hugo Bodory <hugo.bodory@unisg.ch> |
| License | MIT + file LICENSE |
| Version | 1.0.4 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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