Targeted Learning for Survival Analysis

Variable importance through targeted causal inference, with Alan Hubbard

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

Collaborative Targeted Maximum Likelihood Estimation

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

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

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

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.

Targeted MLE with known asymptotic distribution for estimation of survival probabilities from right censored data

TMLE Kernel Methods for Non-Pathwise-Differenetiable Parameters

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

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

Targeted Learning for Survival Analysis

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