CausalState
Causal Inference in a Longitudinal Transitioning State Environment
Implements Sequential Doubly Robust (SDR) and infinite-dimensional Targeted Maximum Likelihood (iTMLE) estimators for longitudinal modified treatment policies in settings with transitioning states, such as ICU, ward, or emergency department care episodes. Treatment is permitted in active states and becomes structurally inapplicable after a state transition (e.g. discharge or death). Supports asymmetric g- and Q-model regularisation, k-fold cross-fitting, and pluggable SuperLearner ensembles. Includes specialised SuperLearner wrappers (SL.tgt.* and SL.tmle_* families) for the iTMLE targeting step, which pass the logit offset as a covariate column to preserve correct subsetting during SuperLearner cross-validation. Methods based on Diaz et al. (2021) <doi:10.1080/01621459.2021.1955691> and Luedtke et al. (2017) <doi:10.48550/arXiv.1705.02459>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.10.2 |
rolling linux/jammy R-4.5 | CausalState_0.10.2.tar.gz |
523.8 KiB |
0.10.2 |
rolling linux/noble R-4.5 | CausalState_0.10.2.tar.gz |
523.8 KiB |
0.10.2 |
rolling source/ R- | CausalState_0.10.2.tar.gz |
169.6 KiB |
0.10.2 |
latest linux/jammy R-4.5 | CausalState_0.10.2.tar.gz |
523.8 KiB |
0.10.2 |
latest linux/noble R-4.5 | CausalState_0.10.2.tar.gz |
523.8 KiB |
0.10.2 |
latest source/ R- | CausalState_0.10.2.tar.gz |
169.6 KiB |
0.10.2 |
2026-04-23 source/ R- | CausalState_0.10.2.tar.gz |
0 B |