📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮
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Updated
Jun 30, 2024 - R
📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮
🌳 🎯 Cross Validated Decision Trees with Targeted Maximum Likelihood Estimation
电气鼠靶场系统是一种带有漏洞的Web应用程序,旨在为Web安全渗透测试学习者提供学习和实践的机会。The Electrical Mouse Target Range System is a web application with vulnerabilities designed to provide learning and practice opportunities for web security penetration testing learners.
A pure Julia implementation of the Targeted Minimum Loss-based Estimation
Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
An R Package for Average Causal Effect Estimation via the Front-Door Functional
📦 R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects
Implementing experiments in paper titled "Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional"
💫 🎯 Automatic identification of variable and interaction importance using basis functions and non-parametric estimation of interactions/effect modification using joint stochastic interventions.
📦 🎲 R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling
🎯🎓 Generalized Targeted Learning Framework
💬 Talk on causal inference and variable importance with stochastic interventions under two-phase sampling
Quarto version of "Introduction to Modern Causal Inference" by Alejandro Schuler.
🎯 📕 Targeted Learning in R: A Causal Data Science Handbook
📦 🎲 R/medshift: Causal Mediation Analysis for Stochastic Interventions
R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
Semiparametric inference for relative heterogeneous vaccine efficacy between strains in observational case-only studies
In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.
Variable importance through targeted causal inference, with Alan Hubbard
npRR: Model-robust inference for the conditional relative risk function using targeted machine learning
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