📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮

Variable importance through targeted causal inference, with Alan Hubbard

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

🎯🎓 Generalized Targeted Learning Framework

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

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

🎯 🎲 Targeted Learning of the Causal Effects of Stochastic Interventions

📦 R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling

📦 R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects

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

🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention

📦 🎲 R/medshift: Causal Mediation Analysis for Stochastic Interventions

💫 🎯 Automatic identification of variable and interaction importance using basis functions and non-parametric estimation of interactions/effect modification using joint stochastic interventions.

Targeted Maximum Likelihood Estimation for Hierarchical Data

Transporting intervention effects from one population to another with targeted learning

🎯 🔀 Targeted Learning for Causal Mediation Analysis

📦 🔬 R/biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery

🌳 🎯 Cross Validated Decision Trees with Targeted Maximum Likelihood Estimation

Data-adaptive creation of exposure (treatment) mixtures using targeted learning

SuperLearner R package: prediction model ensembling method