Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.

Learning algorithms for machine learning based estimation and inference on structural target functions, such as conditional average treatment effects, using influence functions.

Sklearn-style implementations of model selection criteria for CATE estimation

Code to replicate the results in the AISTATS23 paper "Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data" (Curth & van der Schaar, 2023)