Epidemiology analysis package

Variable importance through targeted causal inference, with Alan Hubbard

Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data

SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.

Targeted Learning for Survival Analysis

R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.

Nonparametric estimators of the average treatment effect with doubly-robust confidence intervals and hypothesis tests

A pure Julia implementation of the Targeted Minimum Loss-based Estimation

Doubly-Robust and Efficient Estimators for Survival and Ordinal Outcomes in RCTs Without Proportional Hazards or Odds Assumptions 💊

R/medltmle: Estimation and Inference for Natural Mediation Effect in Longitudinal Data

Targeted Learning entry in the Atlantic Causal Inference Conference's 2017 competition

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.

Transporting intervention effects from one population to another with targeted learning

Collaborative Targeted Maximum Likelihood Estimation

Estimation and Inference for Context-Specific Causal Average Treatment Effect and Optimal Individualized Treatment Effect with Single Time Series

Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"

Estimators of cross-validated prediction metrics with improved small sample performance

Tutorials illustrating the use of baseline information to conduct more efficient randomized trials

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.

R/tstmle01: Estimation and Inference for Marginal Causal Effect with Single Binary Time Series