Adaptive debiased machine learning of treatment effects with the highly adaptive lasso

Code for causal isotonic calibration for heterogeneous treatment effects (appeared in ICML, 2023)

Simulation for "Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics"

Non-parametric variable selection and inference via the outcome-adaptive Random Forest (OARF). Uses the IPTW estimator to estimate the ATE while the propensity score is estimated via OARF. This leads to smaller variance and bias. Only variables that are confounders or predictive of the outcome are selected for the propensity score.

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

Caret Implementation of GRF

Causal inference with multiple treatments

Integrative analyses of the average treatment effect combining big main data and smaller validation data

Simulation of Benkeser D, Cai W, van der Laan MJ (2019+). A nonparametric super-efficient estimator of the average treatment effect.

Approximately balanced estimation of average treatment effects in high dimensions.

R package for fast and easy doubly robust estimation of treatments effects

opttx - Optimal Treatment Estimation