youmisuk/CURobustML: Robust Machine Learning Under Cluster-Level Unmeasured Confounding
A family of machine learning (ML) estimators for handling cluster-level unmeasured confounders in R. It provides a general approach to estimate causal effects in the presence of cluster-level unmeasured confounders in multilevel observational data. In particular, we leverage modern ML methods and exploit a fundamental nature regarding cluster-level unmeasured confounders to estimate the conditional average treatment effect (CATE) and the average treatment effect (ATE). See Suk and Kang (2020) <doi:10.31234/osf.io/t7vbz> for details.
Getting started
Browse package contents
Package details |
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| Maintainer | |
| License | GPL-3 |
| Version | 0.1.1 |
| URL | https://github.com/youmisuk/CURobustML |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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