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

Tutorial for a target trial emulation with a time-varying exposure, time-dependent confounding, time-to-event outcome, and Sequentially Doubly Robust estimation (Hoffman et al. 2022).

DEPRECATED. See new generalized random forest package for up-to-date implementation.

Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)

2SLS Regression with Diagnostics in R

Targeted Maximum Likelihood Estimation for a binary treatment: A tutorial. Statistics in Medicine. 2017

Heterogeneous Treatment Effects

iterative Causal Forest (iCF)

bootstrap confidence intervals for Targeted Maximum Likelihood Estimators

🌳 🎯 Cross Validated Decision Trees with Targeted Maximum Likelihood Estimation

Causal Machine Learning Methods for Differential Variability Analyses in R

Code for the Manuscript: A doubly robust machine learning-based approach to evaluate body mass index as a modifier of the association between fruit and vegetable intake and preeclampsia

An R package for estimating heterogeneous longitudinal modified treatment policy effects

❗ This is a read-only mirror of the CRAN R package repository. causalDT — Causal Distillation Trees. Homepage: https://tiffanymtang.github.io/causalDT/