Adaptive debiased machine learning of treatment effects with the highly adaptive lasso
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Updated
Aug 10, 2023 - R
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)
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.
Approximately balanced estimation of average treatment effects in high dimensions.
Simulation of Benkeser D, Cai W, van der Laan MJ (2019+). A nonparametric super-efficient estimator of the average treatment effect.
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