double robust

In this post, I provide summary notes on the paper “Estimating Heterogeneous Treatment Effects with Right-Censored Data via Causal Survival Forests” by Cui et al. (2023). Motivation How to estimate heterogeneous treatment effects with right-censored data?

Introduction In this post, I will go over how causal forest works based on the tutorial in grf R package. Causal Forests offer a flexible, data-driven approach to estimating varied treatment effects, bridging machine learning and causal inference techniques.

Statistical Setting We observe data $(X_i, Y_i, W_i) \in \mathcal{X} \times \mathbb{R} \times {0,1}$ according to the potential outcomes model and we assume the following: (SUTVA) ${Y_i(0), Y_i(1)} \perp W_i