Causal Effect Estimation via Doubly Robust One-Step Estimators and TMLE in Graphical Models with Unmeasured Variables
Overview
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.
- Initial CRAN release providing doubly robust one-step and TMLE estimators for ADMGs with latent variables.
- Bundled graph functions (
make.graph(),f.parents(), etc.) to streamline identifiability checks. - Added simulated datasets (
data_example_a,data_example_b,data_backdoor) to reproduce README/vignette examples.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK7 OK2026-03-30
Documentation
- Help pages
- 20 (1 internal)
- Examples that run
- 100%15 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 20%
- flexCausal HTML
Downloads
Repository
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Checks run against github.com/annaguo-bios/flexcausal on 2026-10-18.
Dependencies
Installing flexCausal adds 29 packages (16 with compiled code, 18 MB of source tarballs). Heaviest: data.table 5.7 MB, gplots 4.6 MB, mvtnorm 1.0 MB.
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-29 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-03-29
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.1
- Bundled data
- 258 KB / 4 files
- Download size
- 4.5 MB
Cite
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Run in R for the authors' entry:
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