flexCausal: Causal Effect Estimation via Doubly Robust One-Step Estimators
and TMLE in Graphical Models with Unmeasured Variables
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 (may not be active yet) |
| 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 |
Documentation:
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