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

Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.

The R package trajmsm is based on the paper Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories: https://doi.org/10.48550/arXiv.2105.12720.

Positivity violations in marginal structural survival models with time-dependent confounding: a simulation study on IPTW-estimator performance.

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

run causal inference to detect the root causes

An R package for Bayesian Marginal Structural Models