Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
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Updated
Apr 7, 2022 - R
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
Package for heterogeneous treatment and spillover effects under network interference
Code for causal isotonic calibration for heterogeneous treatment effects (appeared in ICML, 2023)
Robust Smooth Heterogeneous Treatment Effect Estimation using Causal Machine Learning
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