Variable importance through targeted causal inference, with Alan Hubbard

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

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

Targeted Learning for Survival Analysis

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

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

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

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.

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

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

TMLE with efficiency guarantees for randomized trials with ordinal outcomes

R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231

SuperLearner R package: prediction model ensembling method

TMLE Kernel Methods for Non-Pathwise-Differenetiable Parameters

🎯 🎲 Targeted Learning and Variable Importance for the Causal Effects of Stochastic Interventions

Targeted Learning for Survival Analysis