Estimation Methods under Unconfoundedness
This section contains methods for estimating (heterogeneous) treatment effects, whose theoretical guarantees are valid only when all potential confounders/controls (factors that simultaneously had a direct effect on the treatment decision in the collected data and the observed outcome) are observed.
- Orthogonal/Double Machine Learning
- Doubly Robust Learning
- Forest Based Estimators