targeted-learning

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

🎯 📕 Targeted Learning in R: Causal Data Science with the tlverse

🎯🎓 Generalized Targeted Learning Framework

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

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

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

🎯 🎲 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

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

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

Website and blog for the research group of Mark J. van der Laan

Transporting intervention effects from one population to another with targeted learning

Targeted Maximum Likelihood Estimation for Hierarchical Data

Course website for "Targeted Learning in Biomedical Big Data" (Spring 2018, UC Berkeley)

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

📦 R/medoutcon: Efficient Causal Mediation Analysis with Intermediate Confounders

📦 🔬 R/methyvim: Targeted, Robust, and Model-free Differential Methylation Analysis

🎯🎓 An introductory workshop lecture on a generalized framework for Targeted Learning using the tmle3 R package

💬 Talk on causal inference and variable importance with stochastic interventions under two-phase sampling

🎯 🔀 Targeted Learning for Causal Mediation Analysis

SuperLearner R package: prediction model ensembling method

Poster "Semiparametric estimation and robust empirical Bayes inference in high-dimensional biological studies" for the annual conference of the Superfund Research Program, November 2019

In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.

💬 Talk on mediation effects based on stochastic interventions

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