CICI-package: Causal Inference with Continuous (Multiple Time Point)...
In CICI: Causal Inference with Continuous (Multiple Time Point) Interventions
CICI-package R Documentation
Causal Inference with Continuous (Multiple Time Point) Interventions
Description
This package facilitates the estimation of counterfactual outcomes for multiple values of continuous interventions at different time points, and allows plotting of causal dose-response curves.
It offers implementations of both the (semi-)parametric g-formula and the sequential g-computation formula. Positivity violations can be detected with diagnostics, and addressed either through feasible intervention strategies, or outcome weights. Details are given in Schomaker et al. (2025) and Bao and Schomaker (2025), see references below.
Details
Package: CICI
Type: Package
Version: 1.0
Date: 2026-04-06
License: GPL-2
Depends: R (>= 4.0)
Imports: mgcv, glmnet, ggplot2, parallel, doParallel, foreach, doRNG, rngtools, SuperLearner, survival
Suggests: haldensify, hal9001
Author(s)
Michael Schomaker, with contributions by Han Bao, Leo Fuhrhop and Katharina Ring
Maintainer: Michael Schomaker <michael.schomaker@stat.uni-muenchen.de>
References
Schomaker M, McIlleron H, Denti P, Diaz I. (2024) Causal Inference for Continuous Multiple Time Point Interventions, Statistics in Medicine, 43:5380-5400, see also https://arxiv.org/abs/2305.06645.
Bao H, Schomaker M (2025) Feasible Dose-Response Curves for Continuous Treatments Under Positivity Violations, arXiv ePrint, https://arxiv.org/abs/2502.14566.
CICI documentation built on April 7, 2026, 5:08 p.m.
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| CICI-package | R Documentation |
Causal Inference with Continuous (Multiple Time Point) Interventions
Description
This package facilitates the estimation of counterfactual outcomes for multiple values of continuous interventions at different time points, and allows plotting of causal dose-response curves.
It offers implementations of both the (semi-)parametric g-formula and the sequential g-computation formula. Positivity violations can be detected with diagnostics, and addressed either through feasible intervention strategies, or outcome weights. Details are given in Schomaker et al. (2025) and Bao and Schomaker (2025), see references below.
Details
| Package: | CICI |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2026-04-06 |
| License: | GPL-2 |
| Depends: | R (>= 4.0) |
| Imports: | mgcv, glmnet, ggplot2, parallel, doParallel, foreach, doRNG, rngtools, SuperLearner, survival |
| Suggests: | haldensify, hal9001 |
Author(s)
Michael Schomaker, with contributions by Han Bao, Leo Fuhrhop and Katharina Ring
Maintainer: Michael Schomaker <michael.schomaker@stat.uni-muenchen.de>
References
Schomaker M, McIlleron H, Denti P, Diaz I. (2024) Causal Inference for Continuous Multiple Time Point Interventions, Statistics in Medicine, 43:5380-5400, see also https://arxiv.org/abs/2305.06645.
Bao H, Schomaker M (2025) Feasible Dose-Response Curves for Continuous Treatments Under Positivity Violations, arXiv ePrint, https://arxiv.org/abs/2502.14566.
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