Provides doubly robust one-step and targeted maximum likelihood (TMLE) estimators for average causal effects in acyclic directed mixed graphs (ADMGs) with unmeasured variables. Automatically determines whether the treatment effect is identified via backdoor adjustment or the extended front-door functional, and dispatches to the appropriate estimator. Supports incorporation of machine learning algorithms via 'SuperLearner' and cross-fitting for nuisance estimation. Methods are described in Guo and Nabi (2024) <doi:10.48550/arXiv.2409.03962>.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.1) |
| Imports: | rlang, dplyr, SuperLearner, densratio, MASS, mvtnorm, stats, utils |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0), earth, ranger |
| Published: | 2026-03-29 |
| DOI: | 10.32614/CRAN.package.flexCausal |
| Author: | Anna Guo [aut, cre] (GitHub: https://github.com/annaguo-bios), Razieh Nabi [aut] |
| Maintainer: | Anna Guo <guo.anna617 at gmail.com> |
| BugReports: | https://github.com/annaguo-bios/flexCausal/issues |
| License: | GPL-3 |
| URL: | https://github.com/annaguo-bios/flexCausal |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | flexCausal citation info |
| Materials: | README, NEWS |
| CRAN checks: | flexCausal results |
| Reference manual: | flexCausal.html , flexCausal.pdf |
| Vignettes: |
flexCausal (source, R code) |
| Package source: | flexCausal_0.1.0.tar.gz |
| Windows binaries: | r-devel: flexCausal_0.1.0.zip, r-release: flexCausal_0.1.0.zip, r-oldrel: flexCausal_0.1.0.zip |
| macOS binaries: | r-release (arm64): flexCausal_0.1.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): flexCausal_0.1.0.tgz, r-oldrel (x86_64): flexCausal_0.1.0.tgz |
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