Epidemiology analysis package

Targeted Learning for Survival Analysis

Variable importance through targeted causal inference, with Alan Hubbard

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

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

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

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

Transporting intervention effects from one population to another with targeted learning

Introduction to Double Robust Estimation for Causal Inference

Collaborative Targeted Maximum Likelihood Estimation

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

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.

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

SuperLearner R package: prediction model ensembling method

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

TMLE with efficiency guarantees for randomized trials with ordinal outcomes

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.

Implementing TMLE in the Presence of a Continuous Outcome