A pure Julia implementation of the Targeted Minimum Loss-based Estimation
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Updated
Aug 21, 2024 - Julia
A pure Julia implementation of the Targeted Minimum Loss-based Estimation
R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
The R package trajmsm is based on the paper Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories: https://doi.org/10.48550/arXiv.2105.12720.
Targeted Learning for Survival Analysis
Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"
Epidemiology analysis package
Semiparametric inference for relative heterogeneous vaccine efficacy between strains in observational case-only studies
Variable importance through targeted causal inference, with Alan Hubbard
Tutorials illustrating the use of baseline information to conduct more efficient randomized trials
npRR: Model-robust inference for the conditional relative risk function using targeted machine learning
Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.
Nonparametric estimators of the average treatment effect with doubly-robust confidence intervals and hypothesis tests
R/medltmle: Estimation and Inference for Natural Mediation Effect in Longitudinal Data
Estimation and Inference for Context-Specific Causal Average Treatment Effect and Optimal Individualized Treatment Effect with Single Time Series
Estimators of cross-validated prediction metrics with improved small sample performance
Doubly-Robust and Efficient Estimators for Survival and Ordinal Outcomes in RCTs Without Proportional Hazards or Odds Assumptions 💊
Transporting intervention effects from one population to another with targeted learning
Collaborative Targeted Maximum Likelihood Estimation
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