R package for Bayesian meta-analysis models, using Stan

Methods for subgroup identification / personalized medicine / individualized treatment rules

This repository provides R-code for the estimation of the conditional average treatment effect (CATE) using machine learning (ML) methods.

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

🎯 🎲 Targeted Learning of the Causal Effects of Stochastic Interventions

CRAN Task View: Causal Inference

Deep Treatment Learning (R)

Tidy methods for Bayesian treatment effect models

Adaptive debiased machine learning of treatment effects with the highly adaptive lasso

📦 🎲 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

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

🎯 🔀 Targeted Learning for Causal Mediation Analysis

Univariate conditional average treatment effect estimation for predictive biomarker discovery

Bounding Treatment Effects by Pooling Limited Information across Observations

Gaines and Kuklinski (2011) Estimators for Hybrid Experiments

Causal Inference in Case-Control Studies

Treatment Effect Heterogeneity visualization using R

Review: Data-driven methodology for detecting treatment effect heterogeneity